Gesture recognition devices and methods

ABSTRACT

Devices and related methods are disclosed herein that generally involve detecting and interpreting gestures made by a user to generate user input information for use by a digital data processing system. In one embodiment, a device includes first and second sensors that observe a workspace in which user gestures are performed. The device can be set to a keyboard input mode, a number pad input mode, or a mouse input mode based on the positioning of the user&#39;s hands. Subsequent gestures made by the user can be interpreted as keyboard inputs, mouse inputs, etc., using observed characteristics of the user&#39;s hands and various motion properties of the user&#39;s hands. These observed characteristics can also be used to implement a security protocol, for example by identifying authorized users by the anatomical properties of their hands or the behavioral properties exhibited by the user while gesturing.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional App. Ser. No.61/602,704, filed on Feb. 24, 2012, which is hereby incorporated byreference in its entirety.

FIELD

The present invention relates to gesture recognition and, in particular,to gesture recognition input devices for digital data processing systemsand related methods.

BACKGROUND

Traditionally, human interaction with digital data processing systems(e.g., personal computers, desktop computers, laptop computers, tabletcomputers, server computers, cell phones, PDAs, gaming systems,televisions, set top boxes, radios, portable music players, and thelike) required physical manipulation of one or more input devices suchas a keyboard or mouse. These systems present a number of ergonomicissues, as the user is required to conform to the fixed geometry of thedevice. For example, traditional input devices have fixed or limitedbutton sizes, which can make interaction with such devices awkwardand/or error prone, especially for those with larger or smaller hands.Traditional input devices can also increase the weight and size of thedigital data processing system, thereby reducing portability and userconvenience. Moreover, physical acquisition can be ungainly, while thefrequent shifting between devices (e.g., between a keyboard, number pad,and mouse) can cause a user to not only physically reset but also toperform mental tasks that can be consciously or unconsciously disruptiveto the user's thought process and concentration.

Moreover, traditional input devices can present a number of securitychallenges. First, unless the system is secured (e.g., by a passwordthat must be entered prior to access), anon-authorized user could use aconventional input device to access the associated digital dataprocessing system. Further, even if the system is password-protected,the traditional input device could be vulnerable to unscrupulous thirdparties who could readily observe keystrokes as a password is entered.Finally, the conventional input device is essentially a passive devicethat most often provides a one-time gating function with no independentability (e.g., apart from recognizing a password) to distinguish betweena truly authorized system user and an imposter, either at the time ofentry of the password, for example, or continually while the usercontinues accessing the system.

Various “virtual keyboard” input devices have been proposed, however,these too suffer from a number of disadvantages. For example, suchsystems rely primarily on detecting only the tip of the user's fingerand calculating the fingertip's velocity in order to determine when a“key” strike occurs. Such systems also generally rely on a static modelin which virtual keys are assigned to fixed locations within aworkspace. Accordingly, such systems focus on the point of impactbetween a user's fingertip and a surface that defines the workspace. Inpractice, however, data regarding the fingertip's velocity at a fixedvirtual location is insufficient to achieve the level of accuracy thatusers need and/or expect from an input device. Moreover, these systemsessentially lock the user into a fixed geometry that presents the sameergonomic issues posed by traditional mechanical keyboards as discussedabove, for example. Further, such systems generally function only in akeyboard mode, or lack a convenient and non-disruptive way to switchbetween available input modes. An exemplary virtual keyboard inputdevice is disclosed in U.S. Pat. No. 6,614,422 to Rafii et al., entitled“METHOD AND APPARATUS FOR ENTERING DATA USING A VIRTUAL INPUT DEVICE,”the entire contents of which are incorporated herein by reference.

In view of the foregoing, there is a need for improved input devices fordigital data processing systems.

SUMMARY

The present teachings generally relate to devices and methods fordetecting and interpreting gestures made by a user so as to generateuser input information for use by a digital data processing system. Bydetecting characteristics of the user's hands and/or various motionproperties of the user's hands in an observed workspace, the exemplarymethods and systems provided herein can reliably interpret the user'svarious gestures as inputs (e.g., keyboard inputs, mouse inputs, etc.)for the digital data processing system. The observed characteristics canalso be used to implement a security protocol, for example, byidentifying authorized users via the anatomical properties of a user'shands and/or the behavioral properties exhibited by the user whilegesturing. Additional object and/or predefined patterns thereon can beidentified and provide additional information and options forinteraction with the device.

In one aspect of the present teachings, an input device for a digitaldata processing system is provided that includes at least one sensorthat observes a workspace and generates data indicative of one or moreparameters of an input agent within the workspace, and a processor thatidentifies gestures made by the agent from the data generated by thesensor(s) and that generates user input information based on theidentified gestures. The user input information generated by theprocessor can represent various types of information such as one or moreof keyboard input information, pointing device input information, andnumber pad input information, all by way of non-limiting example.

Sensors for use in accord with the present teachings can have a varietyof configurations. By way of non-limiting example, one or more sensorscan utilize optical imaging (e.g., image processing), infrared light,structured light, and time-of-flight detection to observe the workspace.For example, a single sensor can generate data indicative of thedistance and/or orientation of portions of the input agent within theworkspace in three-dimensions using time-of-flight signals and/orstructured light (e.g., infrared light and infrared sensor, RGB camera),for example. Further, exemplary systems can comprise multiple sensorsand/or multiple types of sensors used in combination. For example, aprimary sensor can observe the workspace from a first perspective and asecondary sensor of the same or different modality, that can be spaced adistance apart from the first sensor, can observe the workspace from asecond perspective different from the first perspective. In someaspects, the primary sensor can comprise a structured light sensor, thesecondary sensor comprises a camera, and the data generated by theprimary and secondary sensors can be combined to generate a more robustrepresentation of the workspace and the input agent's interactiontherewith. In some aspects, the various perspectives of a first andsecond sensor can together generate a three-dimensional stereoscopicunderstanding of the workspace In various aspects, the processor can beconfigured to generate the user input information without requiringphysical user contact with the input device.

In some aspects, the processor can detect landmarks of the agent (e.g.,specific features, patterns) within the scope of the workspace. Forexample, the agent can comprise a user's hand and the landmarks detectedby the processor can be at least one of a finger, a finger segment, afinger shape, a finger joint, a finger nail, a skin surface contour, anda hand surface. In some aspects, the one or more parameters of the agentcomprise a size of the agent, a color of the agent, a surface texture ofthe agent, a position of the agent, and an orientation of the agent(e.g., the orientation of one or more portions of a user's hand). Invarious aspects, the processor can calculate changes in at least one ofthe parameters of the agent.

In various aspects of the present teachings, the workspace comprises asurface adjacent to the input device and/or a three-dimensional spacewithin the field of view of the at least one sensor. For example, theworkspace can comprise a surface on which the input device is positionedand/or a frontal 180 degree arc extending from the input device. In someaspects of the present teachings, the workspace that can be observed bythe one or more sensors can be based on the position and/or number ofsensors. By way of example, the at least one sensor can comprise aplurality of sensors positioned around the perimeter of the workspace,the workspace comprising a region framed by the plurality of sensors. Insome embodiments, for example, the sensor(s) can be positioned in thecenter of a workspace and outward-facing so as to generate a 360 degreespherical workspace.

In some aspects of the present teachings, the processor can associategestures made by the agent with one or more input candidates such asalphanumeric characters, punctuation marks, symbols, or functionalelements, all by way of non-limiting example. In an embodiment where theprocessor of the input device represents keyboard input information, forexample, the input candidate can provide a function like that typicallyassociated with a specialty key or function key such CTRL, ALT, Page Up,and Page Down.

In various embodiments, the agent can comprise one or more hands, eachof the one or more hands comprising a plurality of fingers. In such anexemplary embodiment, the input device can be operable in a plurality ofoperating modes and the processor can be configured to set a currentoperating mode based at least in part on at least one of a location ofthe one or more hands, a gesture made by the one or more hands, and aconfiguration of the one or more hands. By way of example, the pluralityof operating modes can comprise a keyboard input mode, a pointing deviceinput mode, a number pad input mode, a template-based input mode, and acustom pad input mode. In some aspects, for example, the processor canset the current operating mode to the pointing device input mode whenonly one of the plurality of fingers is extended. Alternatively, forexample, the processor can set the current operating mode to thekeyboard input mode when the processor detects a threshold number ofdigits within the workspace. In some aspects, the processor can set thecurrent operating mode to the number pad input mode when the processordetects movement of one of the one or more hands to a position laterallyoffset from a home position or when the processor detects that only oneof the one or more hands is presented to the workspace. In some aspects,the processor can set the current operating mode to the custom pad inputmode when the processor detects movement of one of the one or more handsto a position laterally offset from a home position or when theprocessor detects that only one of the one or more hands is presented tothe workspace. In some aspects, the processor can be configured to set atemplate-based input mode based at least in part on the identificationof a template and/or object (e.g., tool, stylus, etc.) within theworkspace.

In some aspects, the input devices can include a configuration modulethat assigns particular user input information to a particular gesturesuch that the processor generates the particular user input informationwhen the particular gesture is detected. By way of example, a user'sparticular gesture such as a pinching motion where by the index fingerand thumb are brought together or apart from one another can beassociated with a particular action such as zooming in or out,respectively. Such associations can be pre-defined and/or assigned bythe user. In various aspects, the configuration module displays agesture strength indicator based on a degree to which the particulargesture can be reliably detected.

In accordance with various aspects of the present teachings, an inputdevice for a digital data processing system is provided that includes atleast one sensor for generating data indicative of a workspace and aprocessor. The processor additionally includes a user profile modulethat identifies a plurality of anatomical landmarks of a user's handbased on the data generated by the sensor(s) and determines thelocations of the landmarks within the workspace, a motion detectionmodule that compares the data generated by the sensor over time togenerate a set of values indicative of changes in said landmarks, and aclassification module that associates changes in the landmarks with userinput information.

The motion detection module can compare a variety of data to generatevalues indicative of changes in the landmarks. By way of example, themotion detection module can compare one or more of a distance traveled,a velocity, and an acceleration of a particular landmark to at least oneof a distance traveled, a velocity, and an acceleration of at least oneother landmark such that the classification module can generate userinput information based on said comparison. In some aspects, forexample, the motion detection module can measure changes in distance oflandmarks relative to a starting position and the classification modulecan associate such measurements with user input information.Alternatively or in addition, the motion detection module can measurechanges in velocity and/or acceleration of landmarks. In some aspects,the motion detection module can measure, for example, changes in anangular relationship of at least two landmarks. In some embodiments, forexample, the motion detection module can measure an angular displacementof an angle defined by a vertex and at least two landmarks.Alternatively or in addition, the motion detection module can measure anangular velocity and/or angular acceleration of an angle defined by avertex and at least two landmarks.

In some aspects, the processor can additionally include an orientationmodule that establishes a core value for the user's hand, the core valueindicating a position of the core of the user's hand within theworkspace, for example, based on the observed position and/ororientation of various anatomical landmarks of a user's hand.

In some aspects, systems and methods in accord with the presentteachings can additionally utilize a physical template located withinthe observed workspace and with which the user can physically interactand/or manipulate. In related aspects, the input device can additionallyinclude a template identification module that determines the presenceand position of an object or template within the workspace based on datagenerated by the at least one sensor and identifies the object ortemplate, for example, based on at least one characteristic of theobject, and/or a pattern or marking on the template. Accordingly,alternatively or in addition to associating changes in one or morelandmarks relative to one another, the classification module canassociate changes in landmarks relative to the template, with user inputinformation being associated with the template.

In one aspect of the present teachings, an input device for a digitaldata processing system is provided that includes at least two sensorsspaced a distance apart from one another that observe a workspace andgenerates data indicative of the workspace from at least a first andsecond perspective. The input device can additionally include aprocessor having a user profile module that identifies a plurality ofanatomical landmarks of a user's hand as indicated by the data generatedfrom the at least two sensors to determine a location of the landmarkswithin the workspace. Additionally, the processor can include anorientation calculation module, which calculates a core value thatindicates the position of the core of the user's hand within theworkspace, and a motion calculation module that compares the datagenerated by the plurality of sensors over time to generate a first setof values indicative of distance traveled, velocity, and acceleration ofsaid landmarks. The processor can also include a classification modulethat associates gestures made by the user's hand within the workspacewith user input information based on the first set of values.

By way of example, an input device for a digital data processing systemcan be provided that includes first and second cameras spaced a distanceapart from one another that capture two-dimensional images of aworkspace from first and second perspectives, respectively, and aprocessor. The processor can include a user profile module thatidentifies a plurality of anatomical landmarks of a user's hand withinthe images (e.g., using image processing) and determines a location ofsaid landmarks within the workspace. Based on the data generated by thevarious cameras at different perspectives, an orientation calculationmodule can calculate a core value for the user's hand and the motioncalculation module can generate a first set of values indicative oftwo-dimensional distance traveled, velocity, and acceleration of saidlandmarks. Additionally, the motion calculation module can convert thefirst set of values to a second set of values indicative ofthree-dimensional distance traveled, velocity, and acceleration of saidlandmarks. Based on the second set of values, a classification modulecan then associate gestures made by the user's hand within the workspacewith user input information.

In some embodiments, the motion calculation module can additionallygenerate a third set of values indicative of angular displacement,angular velocity, and angular acceleration (e.g., of an angle having avertex and having a first ray extending from the vertex to a firstlandmark of the plurality of landmarks and a second ray extending fromthe vertex to a second landmark of the plurality of landmarks). In someaspects, the classification module can additionally use this third setof values to associate gestures made by the user's hand within theworkspace with user input information.

In one aspect of the present teachings, a method of authenticating auser of a digital data processing system is provided that includes usingat least one sensor to generate data indicative of one or moreparameters of a user in a workspace, determining gesture informationindicative of a gesture made by the user within the workspace based onthe data, and comparing the gesture information to known gestureinformation particular to the user (e.g., predetermined gestureinformation) to determine whether the user is an authorized user of thedigital processing system. In various aspects, the workspace cancomprise a surface adjacent to the digital data processing system, afrontal 180 degree arc extending from the digital data processingsystem, and/or a surface on which the digital data processing system ispositioned.

In various aspects, the gesture information can comprise changes inconfiguration of the user's hand during entry of a code (e.g., entry ofan alpha-numeric code or string of numbers). By way of example, thegesture information can comprise a speed, a cadence, or a style of theuser's hand movement during entry of a code. Additionally, in variousaspects, the method can include repeating the detecting and comparingsteps each time a hand enters the workspace to determine whether thehand belongs to an authorized user of the digital data processingsystem. Alternatively or in addition, the processor can continuously orintermittently compare the observed gesture information with the knowngesture information particular to the user to ensure that the handbelongs to the authorized user.

In some aspects, methods of authenticating a user of a digital dataprocessing system can further comprise determining one or moreparameters of the user based on the data, and comparing the determinedparameter to a predetermined value(s) to determine whether the user isan authorized user of the digital data processing system. In variousaspects, the parameter can be at least one of an anatomical geometry ofa portion of the user, a color of the portion of the user, and a surfacetexture of the portion of the user (e.g., segments of the hand).Moreover, the detecting and comparing steps can be repeated each time auser enters the workspace to determine whether the user continues to bean authorized user of the digital data processing system. Alternativelyor in addition, the processor can continuously or intermittently comparethe parameter with the predetermined value to ensure that the handbelongs to the authorized user.

In accord with some aspects of the present teachings, a system fordetermining whether a user is an authorized user of a digital dataprocessing system is provided that includes one or more sensors thatdetect gestures made by the user within a workspace and that generategesture information indicative of the detected gestures, and a processorthat compares the generated gesture information to predetermined gestureinformation stored in a storage medium, the processor determiningwhether the user is an authorized user based on a degree to which thegenerated gesture information matches the predetermined gestureinformation.

As discussed otherwise herein, the sensors can have a variety ofconfigurations. For example, in some aspects, the one or more sensorscan be configured to detect the gestures made by the user withoutrequiring physical user contact with the sensors. By way of example, insome embodiments, the one or more sensors can comprise first and secondcameras and the generated gesture information can comprise images of theworkspace captured by the first and second cameras. In relatedembodiments, the processor can detect landmarks of the user within theimages of the workspace. Exemplary landmarks include a finger, a fingersegment, a finger shape, a finger joint, a finger nail, a skin surfacecontour, and a hand surface.

In accord with some aspects of the present teachings, a system forrecognizing an authorized user of a digital data processing system isprovided that includes at least one sensor that detects at least onephysical characteristic of an input agent's hand, and a processor thatcompares the detected physical characteristic to predeterminedcharacteristic information stored in a storage medium, the processordetermining whether the user is an authorized user based on a degree towhich the detected physical characteristic matches the predeterminedcharacteristic information.

In various aspects, the at least one sensor can utilize one or more ofoptical imaging, RGB, infrared light, structured light, andtime-of-flight detection to detect at least one physical characteristicof the user's hand. By way of example, the at least one sensor cancomprise first and second cameras and the detected physicalcharacteristic can be detected from images of a workspace in which theagent's hand is positioned, the images being captured by the first andsecond cameras.

The present invention further provides devices, systems, and methods asclaimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more fully understood from the following detaileddescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1A is a schematic top view of one exemplary embodiment of a gesturerecognition input device resting on a surface;

FIG. 1B is a side view of the gesture recognition input device of FIG.1A;

FIG. 1C is a side view of one exemplary embodiment of a gesturerecognition input device having two structured light sensors;

FIG. 1D is a side view of another exemplary embodiment of a gesturerecognition input device having a structured light sensor;

FIG. 1E is a side view of another exemplary embodiment of a gesturerecognition input device having a camera and a structured light sensor;

FIG. 1F is a side view of another exemplary embodiment of a gesturerecognition input device having a camera and two structured lightsensors;

FIG. 1G is a schematic diagram of the gesture recognition input deviceof FIG. 1A;

FIG. 1H is a schematic illustration of an active zone and a zone ofinterest from the perspective of the sensor(s) of the device of FIG. 1A;

FIG. 1I is a schematic illustration of another exemplary embodiment of agesture recognition input device, in which the input device frames aworkspace;

FIG. 2 is a schematic diagram of a control unit for use with a gesturerecognition device as shown in FIG. 1A;

FIG. 3 is a schematic diagram of exemplary modules that can be includedin the control unit of FIG. 2;

FIG. 4A depicts one exemplary embodiment of a calibration template foruse with a gesture recognition input device;

FIG. 4B depicts a user's hands in a “stand” position during oneexemplary embodiment of a calibration procedure;

FIG. 4C depicts a user's hands in a “spread stand” position during oneexemplary embodiment of a calibration procedure;

FIG. 4D depicts a user's hands in a “ready” position during oneexemplary embodiment of a calibration procedure;

FIG. 5A is a schematic illustration of a coordinate system that can beused to specify particular portions of one or more human hands;

FIG. 5B is a schematic illustration of the coordinate system of FIG. 5Aas applied to a single digit;

FIG. 5C is a schematic illustration of two hands as seen from theperspective of the sensor(s) of the device of FIG. 1A showing exemplaryanatomical landmarks which can be identified by the device;

FIG. 5D is a schematic illustration of two hands as seen from theperspective of the sensor(s) of the device of FIG. 1A showing exemplarycore positions which can be calculated by the device of FIG. 1A andexemplary angles which can be measured by the device of FIG. 1A;

FIG. 5E is a schematic illustration of a left hand from the perspectiveof a sensor of the device of FIG. 1A;

FIG. 6A is a top view of a gesture recognition input device and twohuman hands positioned in a keyboard input mode position within aworkspace of the input device;

FIG. 6B schematically depicts an exemplary image captured by a sensor ofa gesture recognition input device when a user is positioned as shown inFIG. 6A, with various anatomical landmarks identified in the image;

FIG. 7A is a top view of a gesture recognition input device and twohuman hands positioned in a number pad input mode position within aworkspace of the input device;

FIG. 7B schematically depicts an exemplary image captured by a sensor ofa gesture recognition input device when a user is positioned as shown inFIG. 7A, with various anatomical landmarks identified in the image;

FIG. 8A is a top view of a gesture recognition input device and twohuman hands positioned in a mouse input mode position within a workspaceof the input device;

FIG. 8B schematically depicts an exemplary image captured by a sensor ofa gesture recognition input device when a user is positioned as shown inFIG. 8A, with various anatomical landmarks identified in the image;

FIG. 8C depicts an exemplary keyboard template in accord with variousaspects of the present teachings;

FIG. 8D depicts an exemplary special function keypad template having apattern disposed thereon in accord with various aspects of the presentteachings;

FIG. 8E depicts the exemplary special function keypad template of FIG.8D disposed at a different orientation that that of FIG. 8D relative toan exemplary sensor;

FIG. 8F depicts an exemplary object for use in the workspace and havingan exemplary pattern that can be identified and/or tracked by theprocessor;

FIG. 8G depicts another exemplary pattern disposed on the object of FIG.8C;

FIG. 8H depicts an exemplary representation of the projection ofstructured light onto a user's hand in a first position;

FIG. 8I depicts an exemplary representation of the projection ofstructured light on a user's hand in a second position;

FIG. 9A schematically depicts an exemplary image captured by a sensor ofa gesture recognition input device of a user's right hand performing astrike gesture;

FIG. 9B schematically depicts an exemplary image captured by a sensor ofa gesture recognition input device of a user's right hand performing astrike gesture;

FIG. 9C schematically depicts an exemplary image captured by a sensor ofa gesture recognition input device of a user's right hand performing astrike gesture;

FIG. 10A is a graph of angular acceleration as a function of time for anexemplary sequence of key strikes;

FIG. 10B is a graph of angular velocity as a function of time for thesequence of key strikes of FIG. 10A;

FIG. 10C is a graph of angular displacement as a function of time forthe sequence of key strikes of FIG. 10A;

FIG. 10D is a graph of linear velocity as a function of time for thesequence of key strikes of FIG. 10A;

FIG. 10E is a graph of linear acceleration as a function of time for thesequence of key strikes of FIG. 10A;

FIG. 10F is a graph of horizontal distance as a function of time for thesequence of key strikes of FIG. 10A;

FIG. 10G is a graph of horizontal velocity as a function of time for thesequence of key strikes of FIG. 10A;

FIG. 10H is a graph of horizontal acceleration as a function of time forthe sequence of key strikes of FIG. 10A;

FIG. 10I is a graph of vertical distance as a function of time for thesequence of key strikes of FIG. 10A;

FIG. 10J is a graph of vertical velocity as a function of time for thesequence of key strikes of FIG. 10A;

FIG. 10K is a graph of vertical acceleration as a function of time forthe sequence of key strikes of FIG. 10A; and

FIG. 11 is a flow chart depicting one exemplary method of operation of agesture recognition input device.

DETAILED DESCRIPTION

Certain exemplary embodiments will now be described to provide anoverall understanding of the principles of the structure, function,manufacture, and use of the methods, systems, and devices disclosedherein. One or more examples of these embodiments are illustrated in theaccompanying drawings. Those skilled in the art will understand that themethods, systems, and devices specifically described herein andillustrated in the accompanying drawings are non-limiting exemplaryembodiments and that the scope of the present invention is definedsolely by the claims. The features illustrated or described inconnection with one exemplary embodiment may be combined with thefeatures of other embodiments. Such modifications and variations areintended to be included within the scope of the present invention.

To the extent that any of the material in the references incorporatedherein conflicts with the disclosure of this application, the disclosureof this application controls.

Devices and related methods are disclosed herein that generally involvedetecting and interpreting gestures made by a user to generate userinput information for use by a digital data processing system. Invarious embodiments, the exemplary devices include one or more sensorsthat observe a workspace in which a user performs gestures and aprocessor that can interpret the data generated by the sensors asvarious user inputs. In various aspects, a user's gestures, whichincludes for example the configuration of the user's hands, can be usedto set the device to various input modes that can affect the manner inwhich the data generated by the sensors is interpreted. By way ofexample, the device can be set to a keyboard input mode, a number padinput mode, a mouse input mode, or other, customizable input mode, forexample, based on the configuration of the user's hands (e.g., thepresence and/or position of the user's dominant and non-dominant handsrelative to one another). Subsequent gestures made by the user can thusbe interpreted as keyboard inputs, number pad inputs, mouse inputs,etc., using observed characteristics of the user's hands and variousmotion properties of the user's hands. These observed characteristicscan also be used to implement a security protocol, for example, byidentifying authorized users using the anatomical properties of theirhands and/or the behavioral properties exhibited by the user whilegesturing (e.g., while typing in a password or continually orintermittently during use).

Additionally, input devices in accord with various aspects of thepresent teachings can reduce the chance of interruption in mental focusand concentration as the user is not required to switch betweentraditional input devices such as a keyboard, a number pad, and a mouseby providing a unique transition that does not require the user tolocate and acquire a new physical device. Furthermore, input devices inaccord with various aspects of the present teachings can include anorientation component that can ascertain the location of the user'shands in relation to one another and in relation to the input deviceusing a core calculation that based on the various segments of a user'shands. In some aspects, this core calculation can provide a singlevariable based on the observed data that can be used to fine tune manyof the other calculations necessary to dependably and accurately reflectthe user's intent. Moreover, systems and methods in accord with thepresent teachings can also allow tremendous freedom for the user to makeadjustments to their hand positions, thus helping to prevent fatigue,repetitive stress injuries, and other ergonomic concerns. For example,the exemplary input devices disclosed herein can offer the user anon-restrictive experience in which they are freed from the rigidconfines of a fixed input apparatus, and can measure specifically someor all of the user's finger segments, finger joints, and finger nails togenerate a robust positional calculation for X, Y, and Z coordinates ofeach of these anatomical landmarks. In various aspects, such accuratemeasurements of a particular user's motions might not only be useful toidentify the task intended to be performed by the particular user, butalso help develop a data warehouse containing quintessential patternsacross a broad range of users for a particular action. Rather thanrelying solely on velocity, input devices in accord with the presentteachings can account for a plurality of motion variables includingvertical distance, vertical velocity, vertical acceleration, horizontaldistance, horizontal velocity, horizontal acceleration, angulardisplacement, angular velocity, angular acceleration, and so forth, allby way of non-limiting example. Some or all of these features can becombined to provide an input device with a degree of accuracy that issuperior to existing systems.

System Generally

FIGS. 1A-1I illustrate exemplary embodiments of a gesture recognitioninput device 100 that incorporates various aspects of the presentteachings.

Sensors

Gesture recognition input devices in accord with various aspects of thepresent teachings generally include one or more sensors for observing aworkspace and/or generating data indicative of one or more parameters ofan object or input agent present in that workspace (e.g., a user'shand(s), a template, etc.), generally without requiring physical contactbetween the input agent and the input device. By way of non-limitingexample, the sensor(s) can generate data indicative of location,velocity, acceleration, angular displacement, and so forth, of theobject in the workspace that can be used to calculate distance and/ororientation of portions of the object within the workspace, as discussedin greater detail below. Indeed, the sensor(s) can have a variety ofconfigurations and orientations and can operate under a variety ofdetection modalities.

As shown in FIGS. 1A and 1B, for example, the device 100 can includefirst and second sensors 102, 104 for detecting the presence of objects(e.g., a user's hands) within a workspace 106, and can detect motion ofsuch objects. Though two sensors 102, 104 are shown in the illustratedembodiment, it will be appreciated that the device 100 can also haveonly one sensor, or can include more than two sensors. For example, theexemplary device 100 depicted in FIG. 1D includes only a single sensor104. On the other hand, the exemplary device 100 depicted in FIG. 1Fincludes three sensors 102, 103, 104.

Sensors for use in accord with the present teachings can employ any of avariety of sensor technologies or combinations of sensor technologiesknown in the art or hereafter developed and modified with the presentteachings to observe the workspace including, for example, opticalimaging (e.g., visible light cameras), infrared detection, structuredlight detection, and time-of-flight detection. Indeed, exemplary systemscan comprise multiple sensors and/or multiple types of sensors. Forexample, by combining the data generated by multiple sensors thatindividually generate two-dimensional images (e.g., an RGB sensor, a CCDcamera), a stereo three-dimensional “image” can be obtained of theworkspace. Three-dimensional modalities can also be used. For example,by utilizing infrared light to cast a known pattern (e.g., lines, bars,dots, shapes) on the workspace, an IR sensor can capture athree-dimensional image based on the way that the patterned or“structured” light is shaped and/or bent when projected onto the object.Based on the changes in the structure or pattern of the projected light,a three-dimensional understanding of the object can be obtained. Itshould also be appreciated that the projected light can alternativelycomprise known patterns of phase-shifted light or visible light (e.g., apattern of colored light) that can be projected into the workspace anddetected by a sensor in accord with the teachings herein. FIG. 1D, forexample, depicts a device 100 having a single structured infrared lightsensor 104 (as well as a structured infrared light source 105).Alternatively, as described above with reference to two spaced-apart CCDcameras, multiple IR sensors can be placed a distance apart from oneanother so as to provide a more accurate representation of the objectbeing captured (e.g., in the case of occlusion), as shown for example inFIG. 1C. Additionally, in various embodiments, multiple sensors ofvarious modalities can be incorporated into a single device as toprovide additional data regarding objects in the workspace.

For example, whereas in the embodiment depicted in FIGS. 1A and 1B, thefirst and second sensors 102, 104 are both in the form of cameras (e.g.,CCD-based imaging devices) having respective optics for capturing animage from a field of view, the exemplary devices depicted in FIGS. 1C,1E, and 1F additionally or alternatively utilize at least one structuredinfrared light source 105 and one or more infrared sensors. For example,the exemplary device 100 depicted in FIG. 1C includes two structuredinfrared sensors 102, 104 that are configured to detect the interactionof the structured light generated by the infrared light source 105 withone or more objects in the workspace. Alternatively, the exemplarydevices depicted in FIGS. 1E and 1F include multiple sensor types. FIG.1E, for example, depicts a device 100 having one camera (e.g., CCD-basedimaging sensors) 102 and one structured infrared light sensor 104 (aswell as a structured infrared light source 105). FIG. 1F, on the otherhand, depicts a device having one camera (e.g., CCD-based imagingsensors) 103 and two structured infrared light sensors 102, 104 (as wellas a structured infrared light source 105) that are mounted on a surfaceof the device 100 facing the workspace and/or user.

With reference again to FIGS. 1A and 1B, the sensors 102, 104 aremounted on a surface 110 of the device 100 that extends substantiallyperpendicularly from a table, desk, or other work surface 108 on whichthe device 100 is rested, such that the sensors 102, 104 are aimed inthe direction of a user. In this configuration, the collective field ofview of the two sensors 102, 104 defines a workspace 106 that extendsoutward from the device 100 in an approximately 120 degree arc from eachsensor. In some embodiments, the workspace 106 can extend in a broaderor narrower arc, e.g., 90 degrees, 160 degrees, 180 degrees, etc. Theworkspace 106 is three-dimensional, in that it also extends verticallyupwards from the surface 108 on which the device 100 is placed.Accordingly, the workspace 106 can be logically divided into a number ofzones, as shown for example in FIG. 1H. An “active zone” 130 can bedefined as the area in which the user interacts with the surface 108 onwhich the device 100 is resting (or a plane extending from the device ininstances in which the device 100 is not resting on a surface). Inaddition, a “zone of interest” 132 can be defined as the area above theactive zone 130 (e.g., the area that is further away from the surface108 on which the device 100 is resting than the active zone 130). Asdiscussed below, the zone of interest 132 can be monitored to helpclassify certain user gestures, actions, or behaviors. In someembodiments, the active zone 130 can remain fixed relative to the device100 and the sensors 102, 104, while the zone of interest 132 can movewith (e.g., follow) the user's hands 134, 136. In other words, if one orboth of the user's hands 134, 136 move and become askew to the sensors102, 104, the left and/or right portions of the zone of interest 132 canlikewise move. As such, the ability of the sensors 102, 104 to track theuser's gestures within the workspace, whether in the user's interactionwith a surface or in space (e.g., above a surface), can reduce userfatigue and increase usability by allowing the user to shift their handposition for optimum comfort and/or allow the device 100 to be used in avariety of environments. By way of example, in various mobileenvironments, the user may not be presented with a surface that enablesa stable interaction therewith. Regardless, the sensors may nonethelesstrack the user's gestures within the workspace and enable identificationof the user's gestures to generate user input information, as otherwisediscussed herein. On the other hand, if a suitable surface is presentedto the user and the user is so inclined, the user can rest a portion oftheir body (e.g., wrists) on the surface to reduce fatigue withoutinterfering with the sensors ability to observe the user's gesturesabove the surface and/or their interaction with the surface.

It will be appreciated that the quantity of sensors, and/or the sensorpositions and orientations, can be selected to provide a workspacehaving any of a variety of sizes and shapes. For example, the arc of theworkspace 106 can be as small as 1 degree and as large as 360 degrees(e.g., by positioning sensors on more than one side of the device 100).In some embodiments, the device 100 can have an awareness of thedistance between the sensors 102, 104 (i.e., the distance between thesensors can be “known”), such that data generated by the sensors can becombined for subsequent processing.

While the presence of a physical work surface 108 within the workspace106 is generally contemplated herein, the device 100 can also be used inany space, including those in which the workspace 106 does not includeany surface. In addition, the term surface as used herein can refer to aphysical surface or a virtual surface (e.g., a virtual plane). Also,while a frontal sensor perspective is described herein, the device 100can also have other sensor perspectives. For example, as shown in FIG.1I, the device 100′ can be positioned such that the sensors 102′ frame arectangular workspace 106′ such as a white board, desktop, tabletop,display, wall, etc., thereby allowing the user's hands or other objectsbeing manipulated by the user within the workspace 106′ to be viewedfrom a plurality of sides. It will further be appreciated based on thepresent teachings that the workspace can have any shape based on theability of the sensor(s) to detect objects therein. By way of example,one or more sensor(s) can be positioned in the center of a workspace andoutward-facing so as to generate a 360 degree spherical workspacesurrounding the sensors.

Light Source

As discussed above, the sensing modality itself may require theprojection of light into the workspace. By way of example, each of thedevices 100 depicted in FIG. 1C-1F utilize an infrared light source 105to generate a known pattern (e.g., horizontal or vertical lines, bars,dots, shapes) of infrared light on the workspace or a visible lightsource to generate a pattern of colored bars, for example, therebyallowing the one or more sensors to capture the way in which thepatterned light is shaped and/or bent when projected onto the object.The light source 105 can have a variety of configurations forilluminating the workspace. For example, the structured infrared lightsource 105 can be positioned adjacent the IR sensor, or alternatively,can be disposed a distance therefrom.

With reference now to FIGS. 1B, 1E, and 1F, the device 100 canadditionally or alternatively include a light source 112 forilluminating at least a portion of the workspace 106. This can beparticularly advantageous when one or more visible light cameras areused as a sensor and the device 100 is used in an environmentcharacterized by low ambient light levels. Any of a variety of lightsources can be used, such as LEDs or incandescent bulbs. It will beappreciated that data captured by the sensors (e.g., sensors 102, 104 inFIG. 1B) can be processed to determine the lighting conditions in whichthe device 100 is being used and therefore to control whether the lightsource 112 is used and the intensity of the light source 112.

It will also be appreciated in light of the teachings herein, that invarious embodiments, a light source can be configured to project adisplay visible to the user onto the surface. Such a projection could beused to aid in calibration of the device 100 (e.g., by having the userinteract with the surface in a specific manner as otherwise discussedherein), and/or aid novice or unskilled typists, for example, bydisplaying a visual representation of a keyboard onto the surface.Similarly, in various aspects, the projected light could identifyspecific portions of the workspace by color and/or shape that areindicated as corresponding to a particular desired user input. Forexample, as discussed in detail below, a user could assign customizedmeanings to various gestures as they relate to these projected lightpatterns.

Power Switch

The device can also include a power on/off switch 114, which can be asoftware switch or a hardware switch mounted on the exterior of thedevice 100. In some embodiments, depressing the power switch 114 for anextended time period (e.g., three seconds) can power the device 100 onor off, whereas depressing the power switch 114 for a short time period(e.g., one second or less) can trigger an escape or reset operation. Thepower switch 114 can also be used to cause the device to enter or exitone or more operating modes, such as standby, sleep, hibernate, wake,etc.

Control Unit

The device can also include a control unit 116 (which can also begenerally referred to as a “processor”) for controlling the variouselements of the device 100, processing inputs to the device 100, andgenerating outputs of the device 100. FIG. 2 illustrates one exemplaryarchitecture of the control unit 116. Although an exemplary control unit116 is depicted and described herein, it will be appreciated that thisis for the sake of generality and convenience. In other embodiments, thecontrol unit may differ in architecture and operation from that shownand described here.

The illustrated control unit 116 includes a processor 118 which controlsthe operation of the device 100, for example by executing an operatingsystem (OS), device drivers, application programs, and so forth. Theprocessor 118 can include any type of microprocessor or centralprocessing unit (CPU), including programmable general-purpose orspecial-purpose microprocessors and/or any one of a variety ofproprietary or commercially-available single or multi-processor systems.The control unit 116 can also include a memory 120, which providestemporary or permanent storage for code to be executed by the processor118 or for data that is processed by the processor 118. The memory 120can include read-only memory (ROM), flash memory, one or more varietiesof random access memory (RAM), and/or a combination of memorytechnologies. The various elements of the control unit 116 are coupledto a bus system 121. The illustrated bus system 121 is an abstractionthat represents any one or more separate physical busses, communicationlines/interfaces, and/or multi-drop or point-to-point connections,connected by appropriate bridges, adapters, and/or controllers.

The exemplary control unit 116 also includes a network interface 122, aninput/output (IO) interface 124, a storage device 126, and a displaycontroller 128. The network interface 122 enables the control unit 116to communicate with remote devices (e.g., digital data processingsystems) over a network. The IO interface 124 facilitates communicationbetween one or more input devices (e.g., the sensors, a template, a GPSor other location identifying unit, user command button(s)), one or moreoutput devices (e.g., the light source 112, a computer screen,television, user's cell phone or tablet, a graphical display of thevirtual keyboard, etc.), and the various other components of the controlunit 116. For example, the first and second sensors 102, 104 can becoupled to the IO interface 124 such that sensor readings can bereceived and processed by the processor 118. The storage device 126 caninclude any conventional medium for storing data in a non-volatileand/or non-transient manner. The storage device 126 can thus hold dataand/or instructions in a persistent state (i.e., the value is retaineddespite interruption of power to the control unit 116). The storagedevice 126 can include one or more hard disk drives, flash drives, USBdrives, optical drives, various media disks or cards, and/or anycombination thereof and can be directly connected to the othercomponents of the control unit 116 or remotely connected thereto, suchas over a network. The display controller 128 includes a video processorand a video memory, and generates images to be displayed on one or moredisplays in accordance with instructions received from the processor118.

The various functions performed by the device 100 can be logicallydescribed as being performed by one or more modules of the control unit116. It will be appreciated that such modules can be implemented inhardware, software, or a combination thereof. It will further beappreciated that, when implemented in software, modules can be part of asingle program or one or more separate programs, and can be implementedin a variety of contexts (e.g., as part of an operating system, a devicedriver, a standalone application, and/or combinations thereof). Inaddition, software embodying one or more modules can be stored as anexecutable program on one or more non-transitory computer-readablestorage mediums. Functions disclosed herein as being performed by aparticular module can also be performed by any other module orcombination of modules.

In use, the device 100 can detect and/or interpret physical gesturesmade by a user within the workspace 106, and generate corresponding userinput information for use by one or more digital data processing systemsto which the device 100 is coupled (e.g., personal computers, desktopcomputers, laptop computers, tablet computers, server computers, cellphones, PDAs, gaming systems, televisions, set top boxes, radios,portable music players, and the like). The device 100 can be astandalone or external accessory that is operably coupled to the digitaldata processing system, for example using a USB or other communicationsinterface. Alternatively, one or more components of the device 100 canbe formed integrally with the digital data processing system. Forexample, the device 100 can be built into a cellular phone such thatwhen the cellular phone is rested face-up on a table, first and secondsensors 102, 104 positioned on a bottom surface of the cellular phoneare aimed towards a user seated at the table.

Modules

FIG. 3 is a schematic diagram of exemplary control unit modules of oneexemplary embodiment of a gesture recognition input device 100.

Calibration Module

In various embodiments, the device 100 can include a calibration module300 for initially calibrating the device 100 to a particular user. Thecalibration module 300 can calculate the dimensions and properties ofeach segment, joint, nail, etc. of the user's fingers and hands, and cancalculate motion variables as the user performs a calibration routine.Exemplary calibration variables include without limitation verticaldistance, vertical velocity, vertical acceleration, horizontal distance,horizontal velocity, horizontal acceleration, angular displacement,angular velocity, angular acceleration, and so forth.

The calibration module 300 can walk a user through a calibrationprotocol, for example using visible or audible cues instructing the userto perform various calibration gestures. In one embodiment, the device100 can be packaged with a calibration template to assist with thecalibration procedure. FIG. 4 illustrates one exemplary embodiment of acalibration template 400, which can be in the form of a 20″ by 24″ sheetof paper or cardboard. The template 400 includes a device outline 402and a representation 404 of a QWERTY keyboard. The keyboard's 404 homekeys 406 are outlined, and the “S” and “L” keys 408, 410 arehighlighted. A zigzag line 412 is also provided on the template 400,along with two horizontal lines 414, 416.

In an exemplary calibration routine, the template 400 is placed on aflat surface 108 and the device 100 is placed in the marked deviceoutline 402. The user is then instructed to place their hands in a“stand” position, e.g., as shown in FIG. 4B, in which the user's fingersare completely straight and touching one another and in which the tip ofthe user's left middle finger is placed on the highlighted “S” key 408and the tip of the user's right middle finger is placed on thehighlighted “L” key 410, such that the user's fingers and hands extendperpendicularly upwards from the template plane 400. The user is theninstructed to transition to a “spread stand” position, e.g., as shown inFIG. 4C, which is identical to the “stand” position except that theuser's fingers are spread apart from each other. During these steps, thedevice 100 can measure the absolute length and width of the user'sfingers, hands, and the various parts thereof, relying in part on aknown distance D between the template's device outline 402 and thetemplate's keyboard representation 404. In one embodiment, this knowndistance D can be 18. During subsequent operation of the device 100, thesize of a digit or digit segment detected by the sensors 102, 104 can becompared to the size data obtained during the calibration phase with theknown distance D. This can allow the device 100 to estimate the currentdistance between the digit or digit segment and the sensors 102, 104.

Next, the user can be instructed to place their hands in a “ready”position, e.g., as shown in FIG. 4D, in which the user's finger tips areplaced on the home keys 406 of the template while the user's fingers arebent to position the user's palms face down towards the template plane400. During this stage of the calibration routine, the device 100 canconfirm the anatomical dimensions measured earlier and begin performingan orientation calculation, as discussed below with respect to theorientation calculation module 304.

The user can then be prompted to type a predetermined text string orsentence on the keyboard representation 404 of the template 400. As theuser types out the text string, the device 100 can perform orientationand length calculations, calculate a strength rating, and build amovement and behavior profile, as discussed below.

The calibration routine can also instruct the user to assume a “mousehand position” (e.g., curling d2, d3, d4 underneath and that the indexfinger of the dominant hand or the hand that the user prefers to use tooperate a mouse is extended). The user is then instructed to trace thezigzag line 412 with the tip of the extended index finger. During thisphase of the calibration routine, the device 100 can perform 3dimensional calculations and establish a frame of reference in the Zdirection (e.g., the direction extending perpendicular to the devicesurface 110 in which the sensors 102, 104 are mounted and parallel tothe template plane 400, along which the user's hands can move in towardsthe sensors 102, 104 or out away from the sensors). Next, whilemaintaining the “mouse hand position,” the user can be instructed totrace the two horizontal lines 414, 416 with the extended index finger.This phase of the calibration routine allows the device 100 to establisha frame of reference in the X direction (e.g., the direction extendingperpendicular to the Z direction and parallel to the template plane 400,along which the user's hands can move left and right relative to thesensors 102, 104) and in the Y direction (e.g., the direction extendingperpendicular to the template plane 400, along which the user's handscan move up and down relative to the sensors 102, 104). This phase canalso allow the device 100 to refine its frame of reference in the Zdirection.

User Profile Module

In various aspects, systems in accord with the present teachings canacquire and/or store information related to a particular user (e.g., alibrary of data particular to a user). By way of example, once acalibration routine is completed, a user profile module 302 can storeuser profile information unique to the particular user who completed thecalibration routine. This can allow the user to use the device 100 againin subsequent sessions without repeating the calibration procedure,essentially making the calibration procedure a one-time exercise foreach user. Additionally or alternatively (e.g., in the case in which acalibration routine is not performed), the user profile module canacquire and/or store information particular to a user through the user'sinteraction with the system. Such stored information could relate, forexample, to the user's anatomical features and/or commonly-usedgestures, actions, or behavioral patterns. Though the user profilemodule is discussed as being particular to a specific user, in variousembodiments, the data associated with a particular user profile could beshared, for example, to allow for approved secondary users or for thepopulation of a classification module across various users.

In various aspects, the user profile information can be used toauthenticate users of the device, as described below. For example, withreference now to FIGS. 5A-5B, the system can map each segment of auser's hands to a coordinate system specified by a hand (left or right),a digit (D1 through D5), and a segment (S1 through S4). Thus, the tip ofa user's right index finger can be referred to as their right D1S1, andthe base of the user's left pinky finger can be referred to as theirleft D4S3. The system can also map each joint of a user's hands to acoordinate system specified by a hand (left or right), a digit (D1through D5), and a joint (J1 through J3).

In the illustrated embodiment, the distal phalange is mapped to S1, thedistal interphalangeal joint (DIP) is mapped to J1, the middle phalangeis mapped to S2, the proximal interphalangeal joint (PIP) is mapped toJ2, the proximal phalange is mapped to S3, the metacarpophalangeal joint(MP) is mapped to J3, and the metacarpal is mapped to S4. Moreprecisely, the skin overlying the dorsal aspect of each of theseanatomical features is mapped to the indicated reference coordinates.

The system can also map each fingernail of a user's hands to acoordinate system specified by a hand (left or right) and a fingernail(N1 through N5). It will be appreciated that each fingernail typicallycomprises a cuticle, a lunula, and a nail plate, each of which can bedetected by the device 100.

User profile information can include any of a variety of properties ofthe user's hands, such as the size and shape of each of the variouselements shown in FIGS. 5A-5B and the relative positions of each of theelements. User profile information can also include various propertiesof the user's nails, knuckles, scars, tattoos, wrinkles, and so forth.Information indicative of the color and/or texture of the user's skincan also be stored as user profile information.

In addition to the physical properties of the user's hands discussedabove, the user profile module 302 can also be configured to store userprofile information related to a user's tendency to perform particularactions in a particular manner (e.g., patterns, cadence, speed, typingstyle, and so forth). Thus, the user profile could include data obtainedduring normal use or during a calibration procedure regardingcommonly-used gestures, actions, or behavioral patterns. As shown inFIG. 5C, for example, the data generated over time can be processed todetermine which data points (e.g., components of the user's hands) aredetectable in the workspace and to determine the location of said datapoints within the image. By way of example, in the case where one ormore of the sensors comprise imaging sensors, any of a variety of imageprocessing techniques known in the art can be employed to perform suchprocessing, for example as disclosed in Rothganger et al., “3D OBJECTMODELING AND RECOGNITION USING LOCAL AFFINE-INVARIANT IMAGE DESCRIPTORSAND MULTI-VIEW SPATIAL CONSTRAINTS,” International Journal of ComputerVision 66(3), 231-259, 2006, the entire contents of which areincorporated herein by reference. The data acquired from such processingcan be compared with data obtained at different time points tocalculate, for example, various motion properties of each specific datapoint, such as vertical distance, vertical velocity, verticalacceleration, horizontal distance, horizontal velocity, horizontalacceleration, angular displacement, angular velocity, angularacceleration, and so forth, and to determine any behavioral patternsthat can be stored in the user profile module 302.

Orientation Calculation Module

In various aspects, the device 100 can also include an orientationcalculation module 304 that uses information acquired by the calibrationmodule 300 during a calibration protocol and/or information particularto a user (e.g., a non-calibrated user) obtained through the user'sinteraction with the system during use so as to determine the positionaland orientational relationship of the user's hands to one another and tothe device 100. The orientation calculation module 304 can useanatomical parameters, specific user dimensions, angles, movements, andvarious other data to derive one or more values used in subsequentcalculations. By way of example, the orientation calculation module cancalculate a “core” value that represents the center of the dorsum of thehand and can be used to determine hand rotation and/or to distinguishbetween an entire hand moving and just one finger on a hand extending orretracting. In FIGS. 5D-5E, for example, the core location CR of theright hand and the core location CL of the left hand are shown as shadedtriangles. The core value can also help differentiate vibrationsintroduced by the user (e.g., a hand tremor) from vibrations introducedby a common environment of the user and the device 100 (e.g., if bothare traveling in a vehicle on a bumpy road). In some aspects, the corevalue can also contribute to the movement calculations performed by themotion calculation module 324 discussed below, for example by finetuning X, Y, and Z data. The core value can also contribute to theclassification determinations made by the classification module 326, asdiscussed below. The core value can be continuously recalculated andadjusted during operation of the device 100 such that the user can altertheir hand position during continued use, thereby increasing usercomfort while minimizing or eliminating health issues such as fatigueand repetitive stress disorders that can be caused by being forced tokeep the user's hand in the same stance for extended periods.

In one embodiment, the core value can be calculated using measurementsof the angle between various components of the user's hands. Forexample, as shown in FIG. 5D, the orientation calculation module 304 canmeasure any of a variety of angles A1 through A9. The illustrated anglesA1 through A9 are merely exemplary, and it will be appreciated thatangles can be measured between any of the joints or segments of theuser's hands and fingers to calculate a left hand core location CL and aright hand core location CR.

As shown in FIG. 5D-5E, angles formed by a core location CL, CR and anytwo segments, joints, or nails of the user's hands can be used todetermine the hand's orientation relative to the device 100. Theorientation calculation can be factored into the motion andclassification processing discussed below to allow the device 100 to beused with a broad range of hand orientations and to adapt in real timeto changes in hand orientation. Thus, the device 100 does not sufferfrom the constraints and ergonomic issues associated with traditionalfixed-geometry mechanical keyboards and with fixed-geometry virtualkeyboards.

Anatomical Parameters Module

The device 100 can also include an anatomical parameters module 306which stores one or more rules based on the physical constraints ofhuman hands, including their skeleton structure, muscles, and joints.Along with providing a foundation from which the calculations are based,this information can be used to improve device accuracy, for example byadjusting or discarding as spurious any sensor readings that indicatehand positions which are physically impossible. Moreover, when thesensor's view of the entire workspace is occluded, for example, byvarious portions of the user's hand, the anatomical parameters module306 can provide information to assist in the determination of a gesturebased on one or more observable data points.

Mode Selection Module

The device can also include a mode selection module 308 configured toswitch the device 100 between a plurality of operating modes. Exemplaryoperating modes include a keyboard input mode, a number pad input mode,and a mouse input mode. The mode selection module 308 can determine thedesired operating mode by evaluating any of a variety of parameters,such as the user's hand position.

FIG. 6A shows a user's hands positioned in an exemplary “keyboard inputmode position” in which the fingertips are resting on the surface 108within the workspace 106 and the fingers are bent such that the user'spalms are facing down towards the surface 108. In other words, the userplaces their hands in a ready position as they might do when typing on atraditional, physical keyboard. FIG. 6B illustrates an exemplary imagecaptured by one of the sensors 102, 104 when a user assumes the keyboardinput mode position. As shown, the fingernails N1 through N4 and jointsJ1 through J3 of D1 through D4 on both hands are typically visible inthe keyboard input mode position. FIG. 6B also shows the core positionsCL, CR of each hand, which is calculated as described above. When themode selection module 308 detects the user's hands are positioned in thekeyboard input mode position, the current operating mode is switched tothe keyboard input mode.

FIG. 7A shows a user's hands positioned in an exemplary “number padinput mode position.” This position is similar to the keyboard inputmode position, except that the user's dominant hand 708, or the handwhich the user wishes to use for number pad input, is moved forwardsand/or at least slightly outward away from “home” keyboard gestureposition. FIG. 7B illustrates an exemplary image captured by one of thesensors 102, 104 when a user assumes the number pad input mode position.As shown, the fingernails N1 through N4 and joints J1 through J3 of D1through D4 on both hands are still visible in the number pad inputposition, but appear smaller for the non-dominant hand 710 than for thedominant hand 708, due to the placement of the dominant hand 710 closerto the sensors 102, 104. This size difference can be used to helpdistinguish between the keyboard input mode position and the number padinput mode position. In addition to the size difference, the change inhand position (e.g., as indicated by the core position CR, CL for eachhand or the X, Y, and Z coordinates of one or more components of theuser's hands) can be used to determine when mode changes occur. Forexample, the core change that occurs when a user picks up an entire handand moves it to another position in the workspace 106 can be interpretedas an intention to transition to the number pad input mode. Thecalculated core positions CR, CL are also shown in FIG. 7B.

Another exemplary number pad input mode position is similar to thekeyboard input mode position, except that the user's non-dominant hand710, or the hand the user does not wish to use for number pad input, isremoved from the workspace 106. Thus, the number pad input mode can beactivated in several ways. If both hands 708, 710 are already in theworkspace 106, the user can remove the non-dominant hand 710 and proceedwith the dominant hand 708. Additionally, the user can keep both hands708, 710 in the workspace and just move the dominant hand 708 forward.Also, if neither hand is present in the workspace 106, the user canenter the number pad input mode by entering the workspace with only onehand. To return to the keyboard input mode, the user can drop thedominant hand 708 back into the original depth of the keyboard inputmode position and return to typing. Alternatively, the user can reenterthe workspace 106 with the non-dominant hand 710 to resume keyboardinput mode. When the mode selection module 308 detects the user's handsare positioned in the number pad input mode position, the currentoperating mode is switched to the number pad input mode.

FIG. 8A shows a user's hands positioned in an exemplary “mouse inputmode position.” This position is similar to the keyboard input modeposition, except that the index finger D1 of the user's dominant hand808, or the hand which the user wishes to use for mouse input, isextended forwards in the Z direction, towards the device 100. FIG. 8Billustrates an exemplary image captured by one of the sensors 102, 104when a user assumes the mouse input mode position. As shown, D1 on thedominant side is extended, while D2, D3, and D4 remain retracted. The D1fingernail N1 is visible in this position, while the D2, D3, and D4fingernails are not. Generally, a transition to this hand position willbe exhibited by a gradual disappearance of the nails N2, N3, and N4 ofD2, D3, and D4 respectively. In addition, all three joints J1, J2, andJ3 of D1 are visible, whereas only two joints J2, J3 are visible on D2,D3, and D4. Further still, the joints J3 become more prominent when thefingers are retracted. In other words, instead of the joints J3appearing to lie substantially flat in the left-right or X direction,valleys appear between the joints J3 when the fingers are retracted).This information can be used to distinguish the mouse input modeposition from other hand positions. While in the mouse input modeposition, click and drag functions can be accomplished using gesturessimilar to using a physical mouse, except that the user is not actuallyclicking a button. When the mode selection module 308 detects the user'shands are positioned in the mouse input mode position, the currentoperating mode is switched to the mouse input mode. Although thenon-pointing hand is shown in FIGS. 8A-8B, the mouse input mode can alsobe entered and used by presenting only the pointing hand within theworkspace 106.

As noted in the discussion above, the determination as to whichoperating mode is desired by the user can be made by assessing dataoutput from the various sensors discussed above. For example, countingthe number of finger nails present in the data captured by the sensorscan be used to help determine whether one or more fingers are extended,as the nail is generally not visible to the sensors when a finger isretracted. Unique qualities of the fingernail can be used to identifyfingernails within the sensor data. These qualities can include theshape of the nail, the reflectivity of the nail, and the position of thenail (the nail is assumed to be at the tip of the finger). The modedetermination can be augmented by other data interpreted from the sensoroutput, such as changes in distance between joints of a digit that areobserved when the digit is extended as opposed to when the digit isretracted.

It will be appreciated that a variety of user actions can be used as atrigger for transitioning between operating modes. For example, themouse input mode can be entered when two hands are in the workspace andone hand retracts digits D2-D4 while extending D1. The mouse input modecan also be entered when one hand is present in the workspace with D1extended, or when only one hand enters the workspace and D1 on the onehand is extended. Transitions to the mouse input mode can occur from thenumber pad input mode, the keyboard input mode, a custom pad input mode,and so on. By way of further example, the keyboard input mode can beentered when two hands enter the workspace, or when a hand enters theworkspace while another hand is already present in the workspace.Transitions to the keyboard input mode can occur from the number padinput mode, the mouse input mode, the custom pad input mode, and soforth. By way of further example, the number pad input mode can beentered when a dominant hand moves forward while in the keyboard inputmode, when only one hand enters the workspace, or when one hand exitsthe workspace leaving only one hand behind in the workspace. Transitionsto the number pad input mode can occur from the mouse input mode, thekeyboard input mode, the custom pad input mode, and so forth. It will beappreciated that the various input modes described above can functionwith a template present in the workspace, either in conjunction with thetemplate or independently.

Gesture Library Module

The device can also include a gesture library module 310, which canallow the user to define custom gestures for use with various inputmodes discussed here (e.g., the keyboard input, number pad input, and/ormouse input modes). In other words, the gesture library module 310 canstore rules that associate a particular input gesture with a particularoutput behavior. For example, an extended strike duration performedwhile in the keyboard input mode can be interpreted as a custom gesturethat indicates a capital letter is desired. Similarly, a simultaneousstrike of D1 and D2 (i.e., a two-finger tap), can be interpreted as acustom gesture that indicates a backspace or delete operation isdesired. Two-finger taps of extended duration can be interpreted as acustom gesture that indicates a multiple character backspace or deleteoperation. So, for instance, to delete an entire section of text, theuser can transition to the mouse input mode, highlight the text, andperform a two-finger tap. In one exemplary embodiment, a user's gestureof separating his index finger from thumb or bringing his index fingerand thumb together in a pinching motion, within the workspace can beassociated with a particular action such as zooming in or out,respectively.

Gestures defined by a user can be evaluated by the device 100 todetermine how accurately they can be detected, and can be assigned astrength rating, as discussed below with respect to the strength ratingmodule 328. Strength rating information can then be used to inform theuser that a particular gesture is weak, or to suggest alternativegestures that might be stronger.

The gesture library module 310 can also include input modes that rely onthe presence of a “template,” as described below with respect to thetemplate identification module.

Output Module

In various aspects, an output module 312 can define the universe ofpossible outputs that can be generated in response to user inputs,depending on the current operating mode. For example, the possibleoutputs for the keyboard input mode can include upper and lower caseletters A-Z, return/enter, spacebar, capital letter, backspace, tab,function keys. The possible outputs for the number pad input mode caninclude numbers 0-9. The possible outputs for the mouse input mode caninclude point, click, double click, select, drag release, right click,and so forth. For example, in one embodiment, right click can beindicated by moving the index finger a distance to the right and performa single tap. Additional possible outputs can include macros, symbols,punctuation marks, special characters, and other functions. For example,in a keyboard input mode, the user's non-dominant hand can perform agesture for inputting a specialty key or function key such CTRL, ALT,Page Up, and Page Down.

Security Module

The device 100 can also include a security module 314 configured toauthenticate a prospective user of the device 100 or a digital dataprocessing system to which the device 100 is coupled. Utilizing storeduser profile information, the user's unique hand dimensions can be usedalone or in conjunction with other security measures (e.g., a predefinedpassword or gesture) to determine whether the prospective user is anauthorized user as a gating function (e.g., at the time of entry of apassword upon initial entry into a workspace) and/or continually whilethe user accesses the system to ensure that the user remain authorized.

The security module 314 can also include a behavioral component based onthe knowledge that when entering a password a user tends to mimic orrepeat the same patterns, cadence, speed, typing style, and so forth.These behaviors can be stored as part of the user profile informationfor each authorized user, and can be compared to the behaviors of aprospective user to determine whether the prospective user isauthorized. In an extreme example, this can advantageously differentiatebetween identical twins who have the same physical finger structure andcharacteristics. This protocol also has the ability to accommodateinjury or some other unforeseen change in the physical nature of theuser's hands which may affect behavior and/or appearance. The securitymodule 314 can continually or intermittently monitor the user's typingstyle or other behaviors during operation to ensure that the currentuser is still an authorized user. For example, in some embodiments, thedevice 100 can determine that user authentication is required each timethe number of landmarks present in the workspace falls below apredetermined threshold. In other words, when the user removes theirhands from the workspace, the device 100 can become locked, requiringany user that subsequently enters the workspace to be authenticated,e.g., by comparing behavioral or physical attributes of the user tothose of one or more predetermined authorized users. Thus, unlike aone-time password prompt, this can allow the device 100 to preventaccess by unauthorized users when an authorized user unlocks the device100 and then leaves the device 100 unattended. The security module 314can keep track of and augment the behavior information to improveaccuracy and adapt to behavioral changes over time.

Another security advantage provided by the device 100 is the ability toenter a password, social security number, credit card number, or anyother sensitive data into a digital data processing system without theuse of a visible keyboard. This can eliminate the risk of anunauthorized party looking over the shoulder of the user and watchingwhich keys the user strikes to steal the sensitive information.

Template Identification Module

The device can also include a template identification module 316 tofacilitate use of the device 100 with objects other than a user's hands,such as a “template,” which can serve as “a workspace within aworkspace.” Templates can have a variety of configurations but invarious embodiments can be a physical representation of a selected inputmode with which the user can physically interact and/or manipulate. Byway of example, the template can be a physical representation of akeyboard, mouse, number pad, special function key pad, music input(e.g., piano keyboard), drawing/writing implement, etc. In someembodiments, a template can be a plate or card having a surface withfixed key representations (e.g., alpha-numeric keys, keys associatedwith certain pre-defined functions, piano keys), fixed meaning the keyrepresentations have positions that are fixed relative to one another,but not necessarily fixed relative to the sensor(s) or the workspace.This can be used for example in specific industry environments where itis important for the specific commands associated with the template tobe implemented in a more restrictive space (such as in a vehicle or afactory). The standardized key representations provided by the use of atemplate can also be helpful for different typing styles such as the twofinger typist.

A template can be coded with a character, symbol, shape, image, pattern,etc. so as to be identifiable by the template identification modulebased on data generated by the sensors when the template enters theworkspace. By way of example, the template identification module can beconfigured to recognize patterns used in industry (e.g., in the medicalfield) such as a bar codes and Quick Response (QR) codes so as toindicate a template of a particular function or dimension. Withreference to FIG. 8C, for example, an exemplary keyboard template canhave a sensor-facing edge of approximately 1/16^(th) inch that is codedwith a pattern (e.g., an array of shapes, crosses) and that can beidentified by the template identification module 316 as representing akeyboard having particular keys positioned at specific locations withinthe workspace. Alternatively, as shown in FIGS. 8D and 8E, for example,the template can define particular areas which can be associated withparticular special functions such as pre-defined characters, macros,etc. or to which the user can assign specific functions (e.g., F2button, Save, Macro 1). In various aspects, the template identificationmodule 316 can determine the position of the template and/or itsidentity based on the detection of the pattern. By way of example, withreference to FIGS. 8D and 8E, a pattern of crosses on a sensor-facingedge of the template will be altered based on the orientation of thetemplate relative to the device 100. For example, if the template isaskew to the device 100 (FIG. 8D), the observed pattern would appearnarrowing and shrinking relative to that same pattern on a template thatis square to the device (FIG. 8E). The device can then utilize thisdetected pattern to determine the coordinates that represent the one ormore areas on the template that are defined to represent a particularinput. Once the template is identified and its position within theworkspace determined based on its code, for example, the device 100 canequate user gestures with the specific functions or inputs defined onthe template. In some embodiments, the device 100 can be configured torespond only to hands or templates, such that movement of other objects(e.g., writing instruments) within the workspace or a changingbackground can be ignored.

The template identification module 316 can also be configured to detectvarious objects that can be manipulated by the user in the workspace 106and detected by the sensors. For example, the template identificationmodule may be configured to identify an exemplary tool such as a stylus(e.g., pen, pencil) or other instrument that is held by a user based onits size, shape, color, or other identifying characteristics. Further,in some aspects, tools can include a code (e.g., bar code, QR code)and/or pattern that enables the template identification module toidentify the tool and/or determine its position in the workspace.Moreover, as discussed otherwise herein, the processor can track thecode and/or pattern within the workspace so as to provide positional andmovement data from which additional input information can be determined.By way of example, the orientation (e.g., tilt) and speed of a stylusthrough detection of the pattern can be used to calculate the weight,thickness, or intensity of a line that is indicated by the user with thestylus.

With reference to FIGS. 8F and 8G, for example, the templateidentification module 316 can determine the presence of a tool (e.g., adrawing tool) and/or characteristics of the desired input represented bythe tool (e.g., color, line weight, pattern produced by therepresentative drawing tool) based on the identification of a patterndisposed on the tool (e.g., stylus 800). For example, the pattern ofconcentric circles disposed on the exemplary stylus 800 in FIG. 8F couldbe identified by the template identification module 316 as representinga highlighter, for example, whereas the pattern of crosses depicted inFIG. 8G could identify a pen that generates a line of a specified colorand/or line thickness. In some aspects, for example, a stylusrepresenting a pen could be tilted so as to increase the thickness of aline indicated by the motion of the stylus. Moreover, as discussedotherwise herein, the processor can track the movement of the pattern asthe user manipulates the stylus 800 within the workspace so as to allowthe position and orientation of the drawing tool to be tracked and theuser input determined. This can allow the device 100 to be positionedsuch that the workspace 106 includes the surface of a white board,desktop, tabletop, display, wall, etc. and such that the device 100 candetect movement of the tool and interpret such movement as various userinputs. For example, in some embodiments, a plurality of writinginstruments can be provided, each having a unique characteristic,marking, and/or pattern that associates the writing instrument with aparticular type of input information or input attribute (e.g., an inputcolor, an input line weight, and so forth). Further, though the styluses800 are depicted in FIGS. 8F and 8G as having a physical tip 802 (e.g.,a dry-erase tip) that allow the user to actually write on an object(e.g., paper or white board) while the movement of the stylus is alsodetected by the device 100, it will be appreciated that the tip maymerely visually represent to the user the input characteristics of thetool (e.g., color, line weight, etc.). Additionally, in someembodiments, the tool can include a mechanism that enables the user toselect for a desired input. By way of example, a user could depress amechanical button on the stylus 800 such that the pattern displayed bythe stylus 800 as depicted in FIG. 8F is replaced by the patterndepicted in FIG. 8G. In some embodiments, for example, the template canadditionally include one or more controls that allow the user todirectly communicate commands to the processor. For example, in variousembodiments, the user could depress a button that could transmit inputinformation to the processor (e.g., via radio or an IR signal) to undo aprevious gesture. In such a manner, the template identification module316 could therefore indicate the desired input characteristics to beassociated with detection of the stylus' motion.

Error Handling Module

The device 100 can also include an error handling module 318 forimproving overall accuracy of the device 100. For example, the errorhandling module 318 can include a word guesser that is configured toresolve a close call as to which of a plurality potential “keys” hasbeen struck by comparing one or more previous inputs to a dictionary orother reference data source.

Clock Module

The device 100 can also include a clock module 320 for assessing speedof user gestures (which can be used to help determine the specific inputthe user is attempting to provide), or for determining when it isappropriate to place the device 100 in a hibernation mode to conservebattery life. For example, if a predetermined time elapses without userinput, the device can automatically enter a hibernation mode in whichthe sample rate of the sensors 102, 104 is reduced and the light source112 is turned off.

Feedback Module

Alternatively or in addition to an output generated by a digital dataprocessing system resulting from detection by the device 100 of theuser's gestures within the workspace, the device 100 can also include afeedback module 322 configured to provide visible or audible feedback tothe user. In one embodiment, the feedback module 322 can be configuredto display a popup keyboard or other graphical template on a displaydevice. When a strike or other user gesture is detected, the key thatwas struck can be highlighted on the popup representation. Display ofthe popup can be triggered by a user positioning their hands at theready position with no movement for a predetermined time period. Theduration of the time period can be adjusted, and the popup can bedisabled altogether. This feature can be helpful when a user is firstgetting accustomed to operating the device 100, and needs a visual guideto locate “keys.” The reference screen or template can also be displayedin response to a predetermined gesture, which can be stored by thegesture library module 310. The reference screen can also bemode-specific, such that a keyboard is displayed in the keyboard inputmode, a number pad is displayed in the number pad input mode, etc. Othertypes of feedback can also be provided by the feedback module 322, forexample by generating a click or other sound when a strike is detected.

Motion Calculation Module

The device 100 can also include a motion calculation module 324 forassessing motion of a user within the workspace 106. As noted above,data generated by the sensors over time can be processed to derivevertical distance, vertical velocity, vertical acceleration, horizontaldistance, horizontal velocity, horizontal acceleration, angulardisplacement, angular velocity, angular acceleration, changes in size,etc. for one or more components of the user's hands. For example, withreference to FIGS. 8H and 8I, a user's hand having an exemplaryrepresentation of structured light (e.g., visible, IR) projected thereonis depicted as moving from a first position (FIG. 8H) to a secondposition (FIG. 8I). The data generated by a structured light sensorregarding the pattern, size, and shape of each circle of the structuredlight as the user's hand is moved can be used by the processor (e.g.,the motion calculation module) to determine the instantaneousthree-dimensional positioning of the user's hand as well as its movementover time based on changes in the location, size, and shape of thecircles. It will be appreciated that though the structured light in thedepicted embodiment demonstrates a plurality of circles, a variety ofpatterns of structured light could be used (e.g., horizontal lines,vertical lines, colored light bars, etc.). It will be appreciated thatthe structured light can be of the nature of infrared and thus invisibleto the human eye.

Further, though the exemplary motion calculation module 324 can derivethree-dimensional positioning and/or motion of an object in theworkspace based on data derived from a single three-dimensional sensingmodality (e.g., a structured light sensor) as described above, forexample, it will be appreciated that the processor can additionallyreceive data generated by one or more additional sensor(s) of the sameor different modality in order to generate an even more robustrepresentation of the user's interaction of the workspace. In someembodiments, for example, as part of the motion calculation, a stream ofvalues for a set of “active movement variables” can be adjusted based ona core calculation performed by the orientation calculation module 304based on data received from another structured light sensor or animaging sensor so as to fine-tune the processing by the various modules.By way of example, the movement variables can be compared across thevarious sensors. In one embodiment, the current operating modedetermined by the mode selection module 308 can be used to determinewhich of the active movement variables are pertinent to the movementcalculation.

Three dimensional variables can also be calculated by comparing theoutput of the multiple sensors, each of which has a different vantagepoint of the workspace 106. By using two or more imaging sensors 102,104, for example, triangulation or parallax algorithms can be used toobtain a depth calculation to determine how close or far a particularsegment of the user's hands is with respect to the sensors 102, 104.Exemplary techniques for determining the depth of a particular objectbased on stereo images are disclosed in Su at al., “TOWARDS ANEMG-CONTROLLED PROSTHETIC HAND USING A 3-D ELECTROMAGNETIC POSITIONINGSYSTEM,” IEEE Transactions on Instrumentation and Measurement, Vol. 56,No. 1, February 2007; Jain et al., “MACHINE VISION,” Chapter 11, Pages289-308, 1995; Hartley et al., “MULTIPLE VIEW GEOMETRY IN COMPUTERVISION, FIRST EDITION,” Chapter 8, Pages 219-243, 2000; Sibley et al.,“STEREO OBSERVATION MODELS,” University of Southern California, Jun. 16,2003; and Prince et al., “PATTERN RECOGNITION AND MACHINE VISION: STEREOVISION AND DEPTH RECONSTRUCTION,” University College London—ComputerScience Department, 2006, the entire contents of each of which areincorporated herein by reference. Similarly, in some exemplaryembodiments in which data is obtained from the different vantage pointsby a plurality of spatially-separated three-dimensional sensors (e.g.,two structured light sensors with one or more light sources) or from athree-dimensional sensor and a spatially-separated imaging sensor, forexample, the processor can generate a more robust representation of theuser's interaction with the workspace. By way of example, an imagingsensor (e.g., camera) can indicate additional data such as position,movement, color, shape, etc. that is useful in one or more modulesdescribed herein. In some aspects, for example, the data generated bythe various sensors can still provide information regarding portions ofthe workspace that are occluded from detection by one of the sensors.

Classification Module

In various aspects, systems and methods in accord with the presentteachings can include a classification module configured to associatedata generated by the sensors based on the user's gestures in theworkspace with user input information. By way of example, once a motioncalculation module determines a change in position, speed, acceleration,or angle between anatomical landmarks (e.g., segments, knuckles,fingernails) over time, the classification module can associate suchmeasurements with a particular user input. By way of example, theclassification module can determine based on the observed changes inposition, orientation, and/or speed of portions of the user's hand thatthe user's action should be classified as the strike of a particularkey, for example, when operating in a keyboard input mode. Such adetermination could be made, for example, by comparing the observedmotion with that of a library of reference motions.

In various aspects, for example, the device 100 can include aclassification module 326 that interprets the stream of active motionvariables generated by the motion calculation module 324 as a particularuser gesture or action. In other words, the classification module 326can determine which finger is about to strike, where the strike occurs,and whether the strike is indeed a strike.

To determine which finger is about to strike, the classification module326 can track as many fingers as necessary, which preferably is at leastfour on each hand. The classification module 326 can assess any of avariety of attributes of the user's anatomical landmarks, such aschanges in vertical distance between a landmark and the surface 108 onwhich the device is rested, the velocity signature of the landmark, theacceleration of the landmark, or the distance, velocity, or accelerationof the landmark relative to one or more other landmarks. Thus, theclassification module 326 can assess how each finger or finger segmentis moving in relation to other fingers or finger segments. This can helpimprove accuracy when the user is a “concert mover” (i.e., when theuser's typing style is to move multiple fingers when making a singlekeystroke) or when the user is a fast typist (i.e., when the user'styping style is to begin moving a finger for a subsequent keystrokebefore another finger finishes executing a previous keystroke). In someembodiments, the classification module 326 can determine the “fastmover” (e.g., the digit which is accelerating the fastest, has the mostdramatic change in velocity, or has the most deliberate directionalchange angles). The fast mover can be isolated as the currently-strikingfinger in such embodiments. The classification module 326 can alsoassign different weights to user movements that are potential strikecandidates depending on the zone in which the movement occurs or inwhich the movement is initiated. For example, a movement that is apotential strike candidate can be assigned a low weight when themovement is initiated or executed in the zone of interest, whereas asimilar movement initiated or executed in the active zone can beassigned a higher weight. The assigned weighting can be used to decidewhether or not a strike candidate is ultimately classified as a strike.

To determine where a strike occurs, the classification module 326 caninterpret the X, Y, and Z positional data produced by the motioncalculation module 324 in conjunction with the orientation informationgenerated by the orientation calculation module 304. Other motionproperties such as vertical velocity, vertical acceleration, horizontalvelocity, horizontal acceleration, angular displacement, angularvelocity, angular acceleration, and so forth can also be considered.

To determine whether a strike has occurred, the classification module326 can interpret a broad range of data. For example, the classificationmodule 326 can consider the speed with which the user generally types orother behavioral data by referencing user profile information stored bythe user profile module 302. The classification module 326 can alsoexamine the motion variables for certain indicators such as a decreasingvertical distance (e.g., indicating that a fingertip is approaching thesurface within the workspace), vertical velocity signature, linearvelocity signature, acceleration signature, angular velocity signature,and angular displacement signature. Using this information, theclassification module 326 can distinguish between an intended key strike(e.g., a movement typically characterized by a high accelerationfollowed by a sudden drop in acceleration) and a situation in which theuser is merely resting their fingers on the surface 108 (e.g., amovement typically characterized by low accelerations without suddenchanges). The classification module 326 can also determine whether astrike has occurred by determining whether the user movement occurs inthe active zone, the zone of interest, or some other area within theworkspace 106.

The classification module 326 can also consider a digit's previousstatus, which can help reduce errors. For example, the classificationmodule 326 can distinguish between a digit that moves from a “floating”position to a “ready” position and a digit that moves from a “floating”position to a “strike” position.

FIGS. 9A-9C illustrate data generated by one of the sensors 102, 104when a user performs a key strike with the intention of inputting thecharacter “M” into a digital data processing system to which the device100 is coupled. As shown in FIG. 9A, the user's right D1S1 lifts in theY direction from a home position (e.g., a “J” position), out of theactive zone, and into the inactive zone. As shown in FIG. 9B, the user'sright D1S1 then moves in the Z direction away from the sensors and inthe X direction towards D2S1, arriving above an “M” position. As shownin FIG. 9C, the user's right D1S1 then descends in the Y direction in astrike motion from the inactive zone to the active zone, completing thekey strike.

Exemplary motion variables that can be monitored/calculated by thedevice 100 while a user is typing are graphed in FIGS. 10A-10K. In eachgraph, exemplary values are shown for a time period in which the usertypes the character sequence “J U J Y J H J N J M J J” using the device100. In other words, each graph represents the same typing event. Thegraphs illustrate the two-dimensional data generated from a singlesensor. As will be appreciated from the graphs, there can be a slighttime discrepancy between noteworthy events in this two-dimensional dataand the actual user keystrokes. This discrepancy can be largelyeliminated, however, by aggregating two-dimensional sensor data from aplurality of sensors to produce three-dimensional data, and augmentingsaid data with orientation data from a core calculation, as describedherein.

In FIG. 10A, angular acceleration is shown for an angle having the coreCR of the user's right hand as its vertex, a first ray extending fromthe core to D1S1, and a second ray extending from the core to D1S2. Theangular acceleration, expressed in terms of degrees per second persecond, is plotted as a function of time, which is expressed in terms ofimage frame number. Each key stroke is labeled in the graph with acircle. As shown, a sharp positive spike in angular acceleration occursat the timing with which each “key” is struck.

In FIG. 10B, angular velocity is shown for the angle whose accelerationis shown in FIG. 10A. The angular velocity, expressed in terms ofdegrees per second, is plotted as a function of time, which is expressedin terms of image frame number. Each key stroke is labeled in the graphwith a circle. As shown, the timing at which each “key” is struckcorresponds roughly to a midpoint or inflection point between adjacentnegative and positive velocity spikes. The positive spikes in angularvelocity do not occur until after the timing at which each “key” isstruck.

In FIG. 10C, angular displacement is shown for the angle whoseacceleration and velocity are shown in FIGS. 10A-10B. The angulardisplacement, expressed in terms of degrees, is plotted as a function oftime, which is expressed in terms of image frame number. Each key strokeis labeled in the graph with a circle. As shown, a sharp falling spikein angular displacement occurs at the timing with which each “key” isstruck. In other words, angular displacement suddenly reverses coursewhen a key is struck.

In FIG. 10D, linear velocity is shown for each of the core of the user'sright hand, the user's right D1S1, and the user's right D1S2. The linearvelocity of each of these components, expressed in terms of feet persecond, is plotted as a function of time, which is expressed in terms ofimage frame number. Each key stroke is labeled in the graph with avertical line. As shown, each key stroke is accompanied by a negativepeak in linear velocity. The graph also highlights the value incomparing movement of a particular anatomical landmark to movement ofone or more other anatomical landmarks. For example, the velocity ofD1S1 is generally much higher than the velocity of D1S2 during the J toY sequence, whereas the opposite is generally true during the J to Msequence. This is because for a “back” motion (e.g., one in which theuser moves their finger in the Z direction away from the sensors from aJ position to an M position), D1S2 typically pops up in the Y directionfaster and higher than D1S1. On the other hand, for a “forward” motion(e.g., one in which the user moves their finger in the Z directiontowards the sensors from a J position to a Y position), D1S2 typicallydrops much more than D1S1 but D1S1 has a greater velocity.

In FIG. 10E, linear acceleration is shown for each of the core of theuser's right hand, the user's right D1S1, and the user's right D1S2. Thelinear acceleration of each of these components, expressed in terms offeet per second per second, is plotted as a function of time, which isexpressed in terms of image frame number. Each key stroke is labeled inthe graph with a vertical line. As shown, the timing at which each “key”is struck corresponds roughly to a midpoint or inflection point betweenadjacent negative and positive acceleration spikes. The positive spikesin linear acceleration do not occur until after the timing at which each“key” is struck.

In FIG. 10F, horizontal distance is shown for each of the core of theuser's right hand, the user's right D1S1, and the user's right D1S2. Thehorizontal distance of each of these components, expressed in terms offeet, is plotted as a function of time, which is expressed in terms ofimage frame number. Each key stroke is labeled in the graph with avertical line. As shown, the horizontal distance variable can beadditive to the other measurements as one can observe the distancetraveled during a J to Y sequence or during a J to H sequence is muchgreater than, and distinct from, the back motion of a J to N sequence ora J to M sequence. The relationship between these two exemplarylandmarks (D1S1 and D1S2) demonstrates the difference between a forwardand reaching movement and a popping up and back movement. As also shownin the graph, a J to J sequence registers almost no change in horizontaldistance as one would expect when the user is striking the sameposition.

In FIG. 10G, horizontal velocity is shown for each of the core of theuser's right hand, the user's right D1S1, and the user's right D1S2. Thehorizontal velocity of each of these components, expressed in terms offeet per second, is plotted as a function of time, which is expressed interms of image frame number. Each key stroke is labeled in the graphwith a vertical line. As shown, the horizontal velocity tends to beapproximately zero at the moment of each “key” strike.

In FIG. 10H, horizontal acceleration is shown for each of the core ofthe user's right hand, the user's right D1S1, and the user's right D1S2.The horizontal acceleration of each of these components, expressed interms of feet per second per second, is plotted as a function of time,which is expressed in terms of image frame number. Each key stroke islabeled in the graph with a vertical line. The horizontal accelerationvariable can be particularly useful to help interpret user intent. Forexample, the disparity in horizontal acceleration between D1S1 and D1S2can be of particular interest. During a J to Y sequence, for example,the graph shows a signature that confirms reaching forward and up. Thisis in contrast to the J to M sequence, for example, in which thesignature is indicative of an up and back motion. In FIG. 10I, verticaldistance is shown for each of the core of the user's right hand, theuser's right D1S1, and the user's right D1S2. The vertical distance ofeach of these components, expressed in terms of feet, is plotted as afunction of time, which is expressed in terms of image frame number.Each key stroke is labeled in the graph with a vertical line. As shown,the vertical distance of the core remains relatively constant whiletyping the sequence, whereas the segments D1S1 and D1S2 lift in thevertical direction prior to each key strike and then drop in thevertical direction during the actual strike. The disparity between D1S1and D1S2 in this graph is once again indicative of the forward and leftmotion in the J to Y sequence as compared to the J to M sequence inwhich the spike is with the D1S2 variable.

In FIG. 10J, vertical velocity is shown for each of the core of theuser's right hand, the user's right D1S1, and the user's right D1S2. Thevertical velocity of each of these components, expressed in terms offeet per second, is plotted as a function of time, which is expressed interms of image frame number. Each key stroke is labeled in the graphwith a vertical line. As shown, the relationship between D1S1 and D1S2is unique for each key sequence. For example, D1S1 has a greatervertical velocity than D1S2 during the J to Y sequence, whereas theopposite is true for the J to M sequence. Similarly, there is a veryclose mirroring between D1S1 and D1S2 during the J to J sequence.

In FIG. 10K, vertical acceleration is shown for each of the core of theuser's right hand, the user's right D1S1, and the user's right D1S2. Thevertical acceleration of each of these components, expressed in terms offeet per second per second, is plotted as a function of time, which isexpressed in terms of image frame number. Each key stroke is labeled inthe graph with a vertical line. As shown, the vertical acceleration ofthe core remains relatively constant while typing the sequence, whereasthe vertical acceleration of the segments D1S1 and D1S2 spike at thetiming with which each “key” is struck.

The above graphs illustrate the 2D perspective of a single sensor. The3D equivalent of this 2D motion data can be calculated using knownalgorithms, for example as disclosed in the references incorporatedherein.

The device 100 can use one or more of the motion variables describedabove and/or various other information to determine when and where astrike occurs. In some embodiments, the device 100 can determine thefastest mover (e.g., the digit with the most acceleration and/orvelocity) to isolate the currently-striking finger from other fingersthat may be initiating a subsequent strike. The position of thefingertip of the currently-striking finger can then be calculatedrelative to the core to determine which key was struck. For example, ifthe user's right D1 is isolated as the currently-striking finger, theposition of the user's right D1S1 relative to the core can be calculatedto determine which key is being struck. If the right D1S1 moves from a“J” position to a position closer to the core in the Z direction, andtowards D1S2 in the X direction, the gesture can be interpreted as an“M” strike. By way of further example, if the right D1S1 moves from a“J” position to a position that is approximately the same distance fromthe core in the Z direction and away from D1S2 in the X direction, thegesture can be interpreted as an “H” strike.

Alternatively, or in addition, motion of one hand component relative toanother hand component can be analyzed to classify a strike. Forexample, in some embodiments, it can be assumed that the joint J2 of astriking finger drops in the Y direction when the finger is movingforward in the Z direction and lifts in the Y direction when the fingeris moving backward in the Z direction. Because each of these methods hassome inherent error, the system can use both techniques such that theredundancy eliminates most of the error.

Throughout this process, probability-based algorithms can be used toeliminate false strikes (e.g., those which may result when the user is a“concert mover”).

Unlike prior art systems in which only velocity is monitored todetermine when a strike occurs, the device 100 can use a multivariateapproach in which far more accurate strike detection can be obtained. Inparticular, strike classification in the device 100 can be determined atleast in part based on the acceleration of various components of theuser's hands, a motion parameter that tends to have a signature which isrelatively consistent across differing typing styles and hand sizes orshapes. In some embodiments, the classification module can determinewhich key is struck based on a combination of angular displacement,acceleration, linear velocity, and vertical distance.

It will further be appreciated that data generated in one or moredevices in accord with the present teachings can be used to generate aclassification system for use by the same or different classificationmodules. By way of example, rather than simply identifying a particularuser's intended input based on the user's particular movement, theclassification system can generate a data warehouse of typical userinputs so as to continually improve its ability to recognize generalpatterns of a particular behavior, determine or refine anatomical rulesas discussed otherwise herein, and/or improve the classification of anun-calibrated user. By way of example, the classification system can bepopulated with information based on the appearance of a quintessentialkeystroke across a broad range of users so as to reliably classify theintended user input of an unknown user.

Strength Rating Module

The device 100 can also include a strength rating module 328 thatcontinually or intermittently monitors the quality of the sensor databeing acquired. When the strength rating module determines thatdetecting strength is sufficiently weak (e.g., when the user's hands arepositioned in such a way that several digits or segments are obscured byother digits or segments, or are beyond the range of the sensors 102,104), a warning can be issued to the user prompting them to re-orienttheir hands or to move closer to the device 100.

The strength rating module 328 can also be used to assess thereliability of custom gestures defined by the user, as discussed above.The strength rating module 328 can also compare custom gestures to otherstored gestures to make sure each is sufficiently unique to avoid anyadditional chance of error. The calculated strength rating can also beused in a customer service environment to troubleshoot the device 100 orto assist a user that is having difficulty using the device 100. The useof a calculated strength rating in this setting can eliminate the needto divulge other, more sensitive information to a customer servicerepresentative, such as unique hand dimensions or other properties usedby the security module 314.

Adaptability Module

The device 100 can also include an adaptability module 330. Theadaptability module 330 can extend the functionality of the device 100beyond the basic keyboard, number pad, and mouse input modes. Forexample, the adaptability module 330 can allow the user to assigncustomized meanings to various gestures, templates, or a custom pad,each of which can be assessed by the strength rating module 328 and/orstored by the gesture library module 310 as described above. In otherwords, the adaptability module 330 can allow the user to define custommodes (e.g., non-QWERTY keyboard, a foreign language based keyboard) foruse instead of, or in addition to, the standard input modes. This canallow the user to define a custom keypad, for example one that includessymbols, function keys, macros, or even characters in a differentlanguage. The custom pad can be accessed by entering a custom pad inputmode, which in some embodiments can be indicated by a gesture oppositeto that of the number pad input mode. For example, if moving thedominant hand forward and/or slightly outward is effective to transitioninto the number pad input mode, moving the non-dominant hand forwardand/or slightly outward can be effective to transition into the custompad input mode. The adaptability module 330 can also allow the user toassign custom meanings to the keys or buttons defined on a template, orto unique gestures which can be performed without departing from thestandard input modes. Accordingly, the user can customize a templatesuch that it includes one or more commonly-used characters, functions,macros, etc., or assign a particular input or function to a customgesture or input pad, as shown for example in FIGS. 8D and 8E. Forexample, a template having a programmable fixed key could be assigned aparticular function (e.g., a certain note in a piano keyboard, Page Up,commonly used work function, etc.). The template can also be customizedby user-defined sections on the template with only written or printedreferences as to their function.

In various aspects, the adaptability module 330 can provide for changesin the function of the device and/or the interpretation of variousgestures by the classification module, for example, based on variousparameters such as the environment in which the device is operating. Insome embodiments, the adaptability module 330 can identify tags that caninform the processor of the type of function that a user typically usesin a particular environment or location. By way of example, if thecurrent location of the device is identified as being in a public space(e.g., through the inability of the digital data processing system todetect a known or safe wireless network), the adaptability module cangenerate a tag such that indicates to the processor to alter thesecurity settings. For example, if the device is identified as being ina public space, the adaptability module 330 can indicate that theidentity of the user should be continuously monitored, e.g., throughbehavioral and/or anatomical features of the user via gestures in theworkspace. Likewise, if the particular project or document that is beingmanipulated by the data processing system is particularly sensitive, theadaptability module 330 can be configured to increase the monitoring bythe security module.

Additionally, tags can be used to help identify typical functions and/orgestures used in a particular environment. By way of example, rules canbe created by a user or administrator to indicate that if the currentlocation of the device is identified as being at a user's home or in aparticular location (e.g., through the identification of the “home”wireless network or the detection of a signal identifying a specificlocation), a user's particular motions can be interpreted differentlythan if the user is identified as being at the office. For example,though an accountant at their office can have a rule that certaingestures of the left hand is associated with commonly used functionkeys, that same motion at home can be set to be interpreted as adifferent function. For example, when in the kitchen, the accountant'sgesture with the left hand may instead bring up a list of recipes.

Methods

One exemplary method of operation of the device 100 is illustratedschematically in the flow chart of FIG. 11. While various methodsdisclosed herein are shown in relation to a flowchart or flowcharts, itshould be noted that any ordering of method steps implied by suchflowcharts or the description thereof is not to be construed as limitingthe method to performing the steps in that order. Rather, the varioussteps of each of the methods disclosed herein can be performed in any ofa variety of sequences. In addition, as the illustrated flowcharts aremerely exemplary embodiments, various other methods that includeadditional steps or include fewer steps than illustrated are also withinthe scope of the present invention.

As shown, the method can begin with device power-on at step S1000,followed by an initialization step S1002. During the initialization stepS1002, the user can present their hands into the workspace, at whichpoint the device measures the various attributes of the user such as thesize, position, and/or angle of the user's hands, fingers, fingerjoints, finger segments, finger nails, and so forth. Based on thisinformation, a profile is created and optionally stored by the userprofile module 302.

Operation then proceeds to step S1004, in which the profile informationcreated during the initialization step S1002 is used to authenticate theuser. During this step, the security module 314 compares the acquiredprofile information to one or more sets of stored profile informationassociated with authorized users. If the acquired profile informationmatches the profile information of an authorized user, operationproceeds to step S1006. Otherwise, the device 100 is locked to preventuse by an unauthorized user. As noted above, profile information that iscompared for security purposes can include physical attributes of theuser's hands, as well as behavioral traits of the user. It will beappreciated that the sensitivity of the security parameters can beadjusted by the user or an administrator.

At step S1006, the orientation calculation module 304 begins calculatinga core position for each hand that is visible to the sensors 102, 104 ofthe device 100. Using the core position and the angle it forms with thevarious components of the user's hands, the orientation module 304determines the position and orientation of the hands relative to eachother and relative to the device 100.

At step S1008, the motion calculation module 324 determines morereliable X, Y, and Z values for each digit in the workspace by comparingdata from the first sensor 102 and data from the second sensor 104.

At step S1010, the motion calculation module 324 generates data streamsindicative of motion in the X, Y, and Z directions for one or morehands, fingers, or segments in the workspace 106 by comparing successiveimage frames or other snapshots of sensor data. Exemplary motion dataincludes vertical distance, vertical velocity, vertical acceleration,horizontal distance, horizontal velocity, horizontal acceleration,angular displacement, angular velocity, angular acceleration, and thelike.

At step S1012, the mode selection module 308 determines which operatingmode the user desires, and sets the universe of possible outputsaccordingly.

As step S1014, the classification module 326 incorporates the datagenerated in steps S1006 through S1012 to interpret user gestures as“key” strokes, mouse movements, number pad inputs, and so forth. Oncethe gestures are classified, a corresponding output is generated at stepS1016 and provided to one or more digital data processing systems towhich the device 100 is coupled.

Steps S1006 through S1016 are performed continuously or intermittently(but not necessarily at the same rate or in the same order) to monitorthe workspace for user gestures and produce a corresponding output foruse by the digital data processing system.

In view of the foregoing, it will be appreciated that the methods anddevices disclosed herein can free a user from the confines oftraditional input devices. Effortlessly and intuitively, the user canmanipulate technology in a much more comfortable and efficient way. Themethods and devices disclosed herein can also provide various healthbenefits. A user who is “on the go” is no longer required to carryaround extra equipment and the freedom of hand orientation permitted bythe disclosed methods and devices can provide a number of ergonomicbenefits.

In view of the foregoing, it will be appreciated that the methods anddevices disclosed herein can not only provide an initial gating functionprior to granting access to a secure system but also provide uniquesecurity advantages which can directly connect the security functionwith the data that it is intending to secure by repeatedly,intermittently, or continuously confirming the authenticity of the useras the secured data is manipulated. The security function can providecontinuous and, if appropriate, simultaneous levels of security.

Although the invention has been described by reference to specificembodiments, it should be understood that numerous changes may be madewithin the spirit and scope of the inventive concepts described.Accordingly, it is intended that the invention not be limited to thedescribed embodiments, but that it have the full scope defined by thelanguage of the following claims.

What is claimed is:
 1. An input device for a digital data processingsystem, comprising: at least one sensor that observes a workspace andgenerates data indicative of one or more parameters of an input agentwithin the workspace; and a processor that identifies gestures made bythe agent from the data generated by the at least one sensor and thatgenerates user input information based on the identified gestures. 2.The device of claim 1, wherein the user input information generated bythe processor comprises at least one of keyboard input information,pointing device input information, and number pad input information. 3.The device of claim 1, wherein the at least one sensor comprises asingle sensor configured to generate data indicative of thethree-dimensional position and orientation of the agent.
 4. The deviceof claim 3, wherein the sensor comprises a structured light sensor. 5.The device of claim 4, wherein the structured light sensor comprises astructured infrared light sensor.
 6. The device of claim 3, wherein thesensor utilizes time-of-flight detection.
 7. The device of claim 1,wherein the at least one sensor comprises a first sensor that observesthe workspace from a first perspective and a second sensor, spaced adistance apart from the first sensor, that observes the workspace from asecond perspective different from the first perspective.
 8. The deviceof claim 7, wherein the first sensor generates data indicative of thethree-dimensional position and orientation of the agent, and the secondsensor comprises an optical imager.
 9. The device of claim 1, whereinthe processor detects landmarks of the agent.
 10. The device of claim 9,wherein the agent comprises a hand and the landmarks detected by theprocessor comprise at least one of a finger, a finger segment, a fingershape, a finger joint, a finger nail, a skin surface contour, and a handsurface.
 11. The device of claim 1, wherein the one or more parametersof the agent comprise a size of the agent, a color of the agent, asurface texture of the agent, a position of the agent, and anorientation of the agent.
 12. The device of claim 1, wherein theprocessor calculates changes in at least one of the one or moreparameters of the agent.
 13. The device of claim 1, wherein theworkspace comprises a three-dimensional space within the field of viewof the at least one sensor.
 14. The device of claim 1, wherein theworkspace comprises a surface on which the input device is positioned.15. The device of claim 1, wherein the at least one sensor comprises aplurality of sensors positioned around the perimeter of the workspace,the workspace comprising a region framed by the plurality of sensors.16. The device of claim 1, wherein the processor associates gesturesmade by the agent with one or more input candidates.
 17. The device ofclaim 16, wherein the input candidates comprise alphanumeric characters,punctuation marks, symbols, or functional elements.
 18. The device ofclaim 1, wherein the agent comprises one or more hands, each of the oneor more hands comprising a plurality of fingers and wherein the inputdevice is operable in a plurality of operating modes, the processorbeing configured to set a current operating mode based at least in parton at least one of a location of the one or more hands, a gesture madeby the one or more hands, and a configuration of the one or more hands.19. The device of claim 18, wherein the plurality of operating modescomprises a keyboard input mode, a pointing device input mode, a numberpad input mode, a template-based input mode, and a custom pad inputmode.
 20. The device of claim 19, wherein the processor sets the currentoperating mode to the pointing device input mode when only one of theplurality of fingers is extended.
 21. The device of claim 19, whereinthe processor sets the current operating mode to the keyboard input modewhen the processor detects a threshold number of digits within theworkspace.
 22. The device of claim 19, wherein the processor sets thecurrent operating mode to the number pad input mode when the processordetects movement of one of the one or more hands to a position laterallyoffset from a home position or when the processor detects that only oneof the one or more hands is presented to the workspace.
 23. The deviceof claim 1, further comprising a configuration module that assignsparticular user input information to a particular gesture such that theprocessor generates the particular user input information when theparticular gesture is detected.
 24. The device of claim 23, wherein theconfiguration module displays a gesture strength indicator based on adegree to which the particular gesture can be reliably detected.
 25. Aninput device for a digital data processing system, comprising: at leastone sensor for generating data indicative of a workspace; and aprocessor comprising: a user profile module that identifies a pluralityof anatomical landmarks of a user's hand and determines locations ofsaid landmarks within the workspace based on data generated by the atleast one sensor; a motion detection module that compares the datagenerated by the at least one sensor over time to generate a set ofvalues indicative of changes in said landmarks; and a classificationmodule that associates changes in the landmarks with user inputinformation.
 26. The input device of claim 25, wherein the motiondetection module measures at least one of changes in distance of thelandmarks relative to a starting position, changes in velocity of thelandmarks, changes in acceleration of the landmarks, changes in anangular relationship of at least two landmarks, an angular displacementof an angle defined by a vertex and at least two landmarks, an angularvelocity of an angle defined by a vertex and at least two landmarks, andan angular acceleration of an angle defined by a vertex and at least twolandmarks, and wherein the classification module associates suchmeasurements with user input information.
 27. The input device of claim25, wherein the motion detection module measures at least changes invelocity of landmarks and the classification module associates suchmeasurements with user input information.
 28. The input device of claim25, wherein the motion detection module measures at least changes inacceleration of landmarks and the classification module associates suchmeasurements with user input information.
 29. The input device of claim25, wherein the motion detection module measures at least changes in anangular relationship of at least two landmarks and the classificationmodule associates such measurements with user input information.
 30. Theinput device of claim 25, wherein the motion detection module measuresat least an angular displacement of an angle defined by a vertex and atleast two landmarks, and the classification module associates suchmeasurements with user input information.
 31. The input device of claim25, wherein the motion detection module measures at least an angularvelocity of an angle defined by a vertex and at least two landmarks, andthe classification module associates such measurements with user inputinformation.
 32. The input device of claim 25, wherein the motiondetection module measures at least an angular acceleration of an angledefined by a vertex and at least two landmarks, and the classificationmodule associates such measurements with user input information.
 33. Theinput device of claim 25, wherein the motion detection module comparesat least one of a distance traveled, a velocity, and an acceleration ofa particular landmark to at least one of a distance traveled, avelocity, and an acceleration of at least one other landmark, andwherein the classification module generates user input information basedon said comparison.
 34. The input device of claim 25, further comprisinga template identification module that determines the presence of atemplate within the workspace.
 35. The input device of claim 34, whereinthe template is identified based on at least one marking on thetemplate.
 36. The input device of claim 34, wherein the templatecomprises an object that can be manipulated by the user, and wherein theobject is identified based on at least one characteristic of the object.37. The input device of claim 34, wherein the classification moduleassociates changes in landmarks relative to the template with user inputinformation associated with the template.
 38. A method of authenticatinga user of a digital data processing system, comprising: using at leastone sensor to generate data indicative of one or more parameters of auser in a workspace; determining gesture information indicative of agesture made by the user within the workspace based on said data; andcomparing the gesture information to predetermined gesture informationto determine whether the user is an authorized user of the digital dataprocessing system.
 39. The method of claim 38, wherein the gestureinformation comprises changes in configuration of the user's hand duringentry of a code.
 40. The method of claim 38, wherein the gestureinformation comprises a speed, a cadence, or a style of the user's handmovement during entry of a code.
 41. The method of claim 38, furthercomprising repeating the determining and comparing steps each time ahand enters the workspace to determine whether the hand belongs to anauthorized user of the digital data processing system.
 42. The method ofclaim 38, further comprising repeating the determining and comparingsteps to determine if the user is an authorized user during manipulationof data secured by the digital data processing system.
 43. The method ofclaim 38, further comprising: determining a parameter of the user basedon the data from the at least one sensor; and comparing the determinedparameter to a predetermined value to determine whether the user is anauthorized user of the digital data processing system.
 44. The method ofclaim 43, wherein the parameter comprises at least one of an anatomicalgeometry of a portion of the user, a color of the portion of the user,and a surface texture of the portion of the user.
 45. A system fordetermining whether a user is an authorized user of a digital dataprocessing system, comprising: at least one sensor that detects gesturesmade by the user within a workspace and that generate gestureinformation indicative of the detected gestures; and a processor thatcompares the generated gesture information to predetermined gestureinformation stored in a storage medium, the processor determiningwhether the user is an authorized user based on a degree to which thegenerated gesture information matches the predetermined gestureinformation.
 46. The system of claim 45, wherein the processor detectslandmarks of the user.
 47. The system of claim 46, wherein the landmarksdetected by the processor comprise at least one of a finger, a fingersegment, a finger shape, a finger joint, a finger nail, a skin surfacecontour, and a hand surface.
 48. The system of claim 45, wherein theplurality of sensors are configured to detect the gestures made by theuser without requiring physical user contact with the plurality ofsensors.
 49. The system of claim 45, wherein the processor furtherdetermines a physical characteristic of the user and compares thedetected physical characteristic to predetermined characteristicinformation stored in the storage medium.
 50. The system of claim 45,wherein the processor determines whether the user is an authorized userprior to providing access to data secured by the digital data system.51. The system of claim 50, wherein the processor is further configuredto determine whether the user is an authorized user during manipulationof data secured by the digital data processing system.